Disruption of Oncogenic MCL-1-BAX/BAK Interaction Using Integrase Inhibitors: Insights from a Molecular Docking and Dynamic Exploration

Disruption of Oncogenic MCL-1-BAX/BAK Interaction Using Integrase Inhibitors: Insights from a Molecular Docking and Dynamic Exploration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Disruption of Oncogenic MCL-1-BAX/BAK Interaction Using Integrase Inhibitors: Insights from a Molecular Docking and Dynamic Exploration Lateef O. Anifowose, Fikayo N. Adegboyega, Oludare M. Ogunyemi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4412066/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dysregulation of programmed cell death is a hallmark characteristic of cancer cells, making the apoptotic signaling pathway of important clinical relevance in cancer therapy. In mammalian cells, this critical cellular event is negatively regulated by antiapoptotic BCL-2 proteins. Notably, overexpression of Myeloid Cell Leukemia-1 (MCL-1) has emerged as a survival and drug resistance mechanism in several malignancies. Given its high oncogenic potential, MCL-1 represents an attractive therapeutic target for solid and hematological tumors. Oncological drug development is prohibitively expensive, time-consuming, and has a poor success rate due to toxic side effects. Thus, repurposing existing approved drugs with demonstrated safety profiles denotes a promising strategy for rapidly and economically discovering drugs in cancer medicine. Herein, we used a virtual computing technique to screen a customized library of thirty-one antiviral drugs for potential antagonistic activity against MCL-1. Our molecular docking experiment uncovered bictegravir and cabotegravir as promising inhibitors of MCL-1 in comparison to the reference clinical inhibitor (AMG176) based on superior binding affinity and strong interactions with the protein hotspots residues. Further, these integrase inhibitors exhibited appealing pharmacokinetic and toxicity profiles. Noteworthy, the thermodynamic parameters studied during the 100 ns molecular dynamics (MD) simulation and principal component analysis of the MD trajectory exemplify these drugs' structural stability and conformational flexibility in the protein active pocket. Our findings suggest that these integrase inhibitors could be repurposed for cancers overexpressing MCL-1. However, further studies involving experimental biological models are required to unravel their novel anticancer activity and ascertain their clinical efficacy in cancer treatment. MCL-1 Bictegravir BCL-2 Cabotegravir Inhibitors Cancer Drug Repositioning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Apoptosis is a tightly regulated physiological cell death program that plays an indispensable role in the maintenance of normal tissue homeostasis and development throughout life (Galluzzi et al., 2018 ). It is a highly conserved defense mechanism for efficiently eliminating unwanted, damaged, potentially dangerous, or transformed cells in multicellular organisms (Czabotar et al., 2014 ). Hence, the deregulation of this physiological process is considered one of the significant life-threatening and aggressive phenomena in pathological conditions such as cancer (Czabotar et al., 2014 ; Hanahan, 2022 ). The principal signaling pathways of apoptosis in mammalian cells are the extrinsic (death receptor) and intrinsic (mitochondrial) pathways (Hanahan, 2022 ; Hanahan & Weinberg, 2011 ). The extrinsic apoptotic pathway is mediated via extracellular ligand binding to death receptors on cell surfaces such as FasR (fatty acid synthetase receptor), DR3/DR4/DR5 (death receptor 3/4/5), and TNFR (tumor necrosis factor receptor) (Brenner et al., 2015 ). On the contrary, the intrinsic apoptotic pathway is prominently activated by endogenous and exogenous cues (e.g., oxidative stress, oncogene activation, DNA damage, radiotherapy, etc.) and is strictly regulated by the B-cell lymphoma 2 (BCL-2) proteins (Green, 2019 ). The BCL-2 protein family members have at least one BCL-2 homologous domains (BH1-4) and can be broadly grouped into proapoptotic effectors (BAX, BAK, and BOK), pro-survival/antiapoptotic proteins (BCL-2, BCL-W, BCL-xL, A1, and MCL-1), and BH3-only proteins (which can be either an activator [BIM, BID, and PUMA], or sensitizer [BAD, BIK, NOXA, BMF, and HRK]) (Singh et al., 2019 ). This protein family is an essential component of the mitochondria apoptotic pathway. Hence, the precise equilibrium between the anti- and proapoptotic proteins is critical to a cell’s decision between death and life (Bolomsky et al., 2020 ). Under normal cellular circumstances, antiapoptotic proteins bind and sequester apoptotic effectors and BH-3-only activators, thereby blocking the dimerization of BAK and BAX and ensuring cell survival (Bolomsky et al., 2020 ). However, upon stimulation by a stress signal, either or both of the following events ensue. BH-3-only proteins (activators and sensitizers) can directly interact with antiapoptotic proteins, thus facilitating the release of the sequestered BAK and BAX, or free BH-3-only activator proteins can directly bind to and switch on BAK and BAX (Bolomsky et al., 2020 ; Quinn et al., 2011 ). Consequently, this results in the homo-oligomerization of BAK and BAX, which is accompanied by pore formation on the mitochondria membrane (a process described as MOMP – mitochondria outer membrane permeabilization), cytochrome C release into the cytoplasm, apoptosome formation, caspase cascade activation, and finally cell death (Roy et al., 2014 ; Singh et al., 2019 ). In cancerous cells, the relative ratios of BCL-2 protein members in the mitochondria membrane are more inclined towards antiapoptotic proteins than apoptotic effectors, making the former a high-priority therapeutic target in various malignancies (Delbridge & Strasser, 2015 ). Myeloid cell leukemia-1 (MCL-1) is a prominent representative of the BCL-2 antiapoptotic protein family (H. Wang et al., 2021 ). It is crucial in many cells' development and survival, including B- and T- lymphocytes, cardiomyocytes, and the nervous system (Fogarty et al., 2019 ; Opferman et al., 2003 ; X. Wang et al., 2013 ). Three spliced variants of MCL-1 (MCL-1S, MCL-1ES, and MCL-1L) exist, each having a distinct role in apoptosis (H. Wang et al., 2021 ). The most extended variant is MCL-1L, encoded by the exon I-III genes (H. Wang et al., 2021 ). It acts as an antiapoptotic factor and is traditionally regarded as MCL-1 (H. Wang et al., 2021 ). MCL-1 has three putative BH domains, and its larger size of about 350 residues distinguishes it from its pro-survival relatives (Thomas et al., 2010 ). The overamplification of the MCL-1 gene is a typical aberration protecting various hematological and solid tumors from apoptosis (Belmar & Fesik, 2015 ). It is understood to contribute to survival in NSCLC (Wen et al., 2019 ), liver cancers (Akgul, 2009 ), melanoma (Boisvert-Adamo et al., 2009 ), AML (acute myeloid leukemia), lymphoma, and plasma cell myeloma cell lines (Phillips et al., 2015 ; Slomp & Peperzak, 2018 ; Tron et al., 2018 ). Further, MCL-1 gene (1q21.2) amplification has been touted as a resistance strategy against a plethora of established anticancer therapies, including resistance to lapatinib in HCT116 cells (Martin et al., 2008 ), resistance to prednisone in mixed lineage leukemia-rearranged infant ALL (Stam et al., 2010 ), cisplatin-resistant in ovarian carcinoma cells (Simonin et al., 2009 ), radiotherapy, and BCL-2/BCL-xL targeting drugs (Hird & Tron, 2019 ; Shahar & Larisch, 2020 ). Thus, from a clinical standpoint, MCL-1 is an essential molecular target in cancer therapy. Due to their genetically elevated levels in human cancers (Beroukhim et al., 2010 ), as well as numerous implications in promoting cancer survival and drug resistance, a few compounds (S64315 [NCT03672695], AZD5991 [NCT03218683], PRT1419 [NCT04543305, NCT04837677], and AMG176 [NCT02675452]) that target MCL-1 are currently undergoing clinical evaluation (ClinicalTrials.Gov). Heretofore, none has been authorized for use in cancer treatment. Emphasizing the need to increase the momentum in our search for potent, safe, and selective MCL-1 inhibitors that could be used either as a single regime or in combination therapy. Developing novel oncological drugs is an intense, time- and money-consuming process with high developmental risk involving comprehensive pre-clinical and clinical studies (Turanli et al., 2021 ). Thus, exploring the anticancer benefits of approved non-cancer drugs for targeted therapy in cancer represents an advantageous approach over the conventional de novo drug discovery due to its cost-effectiveness, reduced development time, and lower risks for cancer patients (Ohmoto & Fuji, 2021 ). Thalidomide, arsenic trioxide, and ATRA (all-trans retinoic acid) are best-known examples of FDA-approved repurposed drugs considered as standard treatment in the National Comprehensive Cancer Network (NCCN) guidelines (Gonzalez-Fierro & Dueñas-González, 2021 ). Of late, molecular docking has become a valuable tool in uncovering novel activity or targets for established drugs (Pinzi & Rastelli, 2019 ). Herein, we screened thirty-one approved antiviral medicines for potential antagonistic activity against MCL-1 using a virtual computing technique. 2. Materials and Methods 2.1 Ligand search and preparation for docking We searched for antiviral drugs on the DrugBank database ( https://go.drugbank.com/ ), and thirty-one (31) antiviral drugs currently in use in clinical settings were randomly selected. The three-dimensional (3D) SDF (structure data file) conformers of these ligands and the reference drug (AMG176) were obtained from the PubChem database ( http://pubchem.ncbi.nlm.nih.gov ), and PyRx OpenBabel was used to minimize energy and convert the ligands to AutoDock Ligand format (PDBQT) (Dallakyan & Olson, 2015 ). 2.2 Protein preparation and active site determination The pro-survival MCL-1 protein (6OQB) coordinate was recruited from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB - https://www.rcsb.org/ ), and the protein repair and analysis server (PRAS) ( https://www.protein-science.com/ ) was used to add missing residues, polar hydrogen atoms, and delete water molecules (Nnyigide et al., 2022 ). The BIOVIA Discovery Studio (BIOVIA, Dassault Systѐmes, Visualizer, v21.1.0.20298, San Diego: Dassault Systѐmes, 2021) was used to remove co-crystallized compound that could impact virtual screening, and the protein active site amino acid residues were detected using Computed Atlas of Surface Topography of proteins (CASTp) online web server ( http://sts.bioe.uic.edu/ ) (Tian et al., 2018 ). Accordingly, these amino acid residues were noted and used for site-specific docking. 2.3 Docking validation (Redocking) Prior to our molecular docking experiment, our virtual screening software (PyRx) was validated to measure its ability to reproduce the ligand conformation in the crystallographic structure of 6OQB. Briefly, the bound ligand (N0J/AM-8621) was separated from the protein using BIOVIA Discovery Studio (BIOVIA, Dassault Systѐmes, Visualizer, v21.1.0.20298, San Diego: Dassault Systѐmes, 2021), and saved as a PDB file. The PyRx virtual screening tool was then used to redock the co-crystallized ligand back into the active pocket of the protein (6OQB), as demonstrated in our previous study (Anifowose et al., 2023 ). And the RSMD (Root Mean Square Deviation) value was computed using MGLTools (ADT 1.5.7 software) (Sanner, 1999 ). 2.4 Molecular docking and post-docking analysis The molecular docking experiment was run using the PyRx (0.8) virtual screening software (Dallakyan & Olson, 2015 ). Accordingly, the receptor grid box was adjusted to enclose the defined active site residues while maintaining the default grid box dimension (25x25x25) and exhaustiveness (8) throughout the docking procedure. Further, post-docking analysis was carried out to investigate the protein-ligand interaction using BIOVIA Discovery Studio (BIOVIA, Dassault Systѐmes, Visualizer, v21.1.0.20298, San Diego: Dassault Systѐmes, 2021). 2.5 Physicochemical properties and ADMETox evaluation The ADMET properties of the top two hits were assessed using ADMETlab 2.0 webserver ( https://admetmesh.scbdd.com/ ) (Xiong et al., 2021 ). Diverse parameters in line with pharmacokinetics and toxicological endpoints were examined. 2.6 Molecular dynamics simulation Desmond software from Schrödinger LLC (Bowers et al., 2006 ) was employed for the 100 nanoseconds MD simulation of the receptor-ligand complexes. By incorporating Newton’s classical equation of motion, we investigated the structural integrity of the protein and protein-ligand complexes in a physiological environment over time (Hildebrand et al., 2019 ). The system builder tool was used to prepare the complex systems for simulation. The TIP3P (Transferable intermolecular potential 3 points) water model with an orthorhombic box and OPLS_2005 force field was adopted (Shivakumar et al., 2010 ). Further, counter ions were introduced to neutralize the system, and 0.15 M sodium chloride (NaCl) was added to mimic a physiological condition. The isothermal-isobaric ensemble (constant number of particles N, pressure P, and temperature T – NPT) of the Nose–Hoover thermostat (Jorgensen et al., 1983 ) and Martyna–Tobias–Klein barostat was deployed to maintain the system’s temperature and pressure at 310 K and 1 bar, respectively. The trajectory data collection was set at 100 picoseconds (ps) intervals, and the model system was relaxed before the simulation experiment. Accordingly, the spectrum of various thermodynamic parameters, including root mean square deviation, root mean square fluctuation, the radius of gyration (RoG), solvent accessible surface area, and the number of hydrogen bonds, was plotted as a time frame function. 2.7 Binding free energy determination and principal component analysis The prime MMGBSA (molecular mechanics generalized Born surface area) module of Maestro 12.8 (Schrödinger Release 2021-2: Maestro, Schrödinger, LLC, New York, NY, 2021) was used to estimate the binding free energy change (∆G Bind) of the hit molecules before (0 ns) and after (100 ns) MD simulation. The VSGB solvent model, OPLS_2005 force field, and rotamer search techniques were adopted to calculate the complex systems' total free energy of binding. The principal component analysis (PCA) was performed using the Bio3D R package (Grant et al., 2021 ). The R language script was used to compute the PCA from the Desmond MD trajectories (Kitao, 2022 ; Palma & Pierdominici-Sottile, 2023 ). 3. Results and Discussion 3.1 Molecular docking validation 3.2 Molecular docking analysis After validating the screening protocol, the antiviral drugs and the reference compound (AMG176) were docked in the active pocket of MCL-1 to investigate their binding affinity for the protein target. The clinical inhibitor (AMG176) has a binding energy of -7.7 kcal/mol, and this value was set as the cut-off point to identify potential hits that could target MCL-1 hydrophobic BH-3 binding grooves with high affinity. A lower binding energy corresponds to a higher binding affinity of a ligand for the protein target (Simon et al., 2017 ). Thus, ligands having a binding energy superior to the reference compound were considered hits (Table 1 ). Several antiviral drugs displayed binding energy exceeding the reference compound's (AMG176). However, the small-molecule integrase strand transfer inhibitors (bictegravir and cabotegravir) exhibiting a greater affinity for MCL-1 protein were considered for the molecular dynamic simulation study. Table 1 Binding energy of promising MCL-1 inhibitors and reference compound S/N PubChem ID Drug name Binding affinity (kcal/mol) 1 90311989 Bictegravir -9.7 2 54713659 Cabotegravir -9.3 3 11527519 Elsulfavirine -8.2 4 9604654 Pradefovir -8.1 5 491941 Pritelivir -8.0 6 71345 Pirodavir -7.9 7 5625 Delavirdine -7.8 8 118910268 AMG176 (reference compound) -7.7 Developing effective MCL-1 inhibitors has proven difficult (Wan et al., 2018 ). The large size and high lipophilicity of previously identified MCL-1 putative inhibitors have resulted in discouraging drug-like properties, including poor pharmacokinetic profile, limited cell membrane permeability, and lack of specificity (Arnold et al., 2008 ; Mohammad et al., 2007 ; Oliver et al., 2004 ). Fortunately, several compounds with demonstrated efficacy in preclinical studies have progressed to clinical trials in recent years. Howbeit, to the best of our knowledge, none has been recommended for use in cancer treatment. Hence, the unmet therapeutic needs of cancer patients with genetically elevated levels of MCL-1 (Derenne et al., 2002 ). MCL-1 hydrophobic BH-3 binding groove lacks structural plasticity (Caenepeel et al., 2018 ). The key binding pocket on MCL-1 is shallow and rigid compared to the binding site of other antiapoptotic proteins (BCL-2 and BCL-xL), creating a fundamental challenge for scientists in developing MCL-1 inhibitors. The Arg263 and four hydrophobic pockets (P1-P4) are considered hotspots of the MCL-1 protein (Table 2 ) (Denis et al., 2020 ). However, nuclear magnetic resonance-based screening showed that inhibitors that bind with the region (P2 pocket) that form a large hydrophobic cavity upon ligand binding appear more potent (Belmar & Fesik, 2015 ). Table 2 MCL-1 hotspots residues P1 P2 P3 P4 MCL-1 Leu235 Leu246 Val249 Met231 Val253 Phe254 Leu267 Phe270 Met250 Gly271 Val274 Leu290 Ile294 His224 Ala227 Phe228 Thr266 Val216 Val220 Val265 Arg263 A closer look into the binding mode of bictegravir and cabotegravir with the hydrophobic BH-3 binding grooves of MCL-1 protein revealed interesting protein-ligand interactions, as summarized in Table 3 . Bictegravir binds mainly in the P1 and P2 pockets of MCL-1 protein through pi-interactions (Leu246, Val253, Arg263, Val274, Met250, and Phe270), carbon hydrogen (Gly271), and halogen (Leu267) bond, and form a conventional hydrogen bond with Arg263 (Fig. 2 a). On the other hand, Fig. 2 b shows that cabotegravir primarily contacts the P1, P2, and P3 pockets via pi-interactions (Leu 246, Val253, Met250, and Phe270) and carbon-hydrogen bonds (Thr266 and Gly271), while also forming two conventional hydrogen bonds with Arg263. However, the reference compound (AMG176), specifically binds to residues in the P2 and P3 pockets mainly through pi-interactions (Ala227, Phe228, Met231, Val253, Leu267, and Phe270) and carbon hydrogen bond (His224) (Fig. 2 c). But no hydrogen bond was observed between AMG176 and Arg263. The filling of the P2 binding region and additional hydrogen bond with Arg263 allows a tighter interaction of bictegravir and cabotegravir with MCL-1 protein, thus increasing their inhibitory activity. Highlighting the strong potential of these integrase inhibitors to disrupt the protein-protein (MCL-1-BAX/BAK) interactions promoting cancer cell survival and drug resistance. Table 2 Molecular interactions of hits and reference compound S/N Drug name Interacting residues Conventional H-bond 1 Bictegravir Leu246, Met250, Val253, Leu267, Phe270, Gly271, and Val274 Arg263 2 Cabotegravir Leu 246, Met250, Val253, Thr266, Phe270, and Gly271 Arg263 3 AMG176 (Reference compound) His224, Ala227, Phe228, Met231, Val253, Leu267, and Phe270 - 3.3 Physicochemical properties and ADMETox evaluation Herein, we further appraise the pharmacokinetic properties of the hits that earned them the U.S. FDA (Food and Drug Administration) approvals in 2018 (Bictegravir) and 2021 (Cabotegravir) as monotherapy in combination with other antiretroviral agents in HIV infection. Importantly, to gain insight into their specific ADME behavior and toxicological parameters as a single agent early in the drug discovery pipeline. According to Lipinski's rule of five (LR5), a small-molecule drug should not flout more than one of the under-listed criteria: molecular weight < 500 Da, hydrogen bond acceptor (N or O) ≤ 10, an octanol-water partition coefficient (log P) ≤ 5, and hydrogen bond donor (N.H. or O.H.) ≤ 5 (Lipinski, 2004 ). Interestingly, from Table 3 . below, it could be inferred that bictegravir and cabotegravir violated none of the rules, hence their conformance to the LR5 in addition to the Pfizer's (logP > 3; TPSA < 75) and Golden Triangle (200 ≤ M.W. ≤ 50; -2 ≤ logD ≤ 5) rules. Suggesting that bictegravir and cabotegravir are expected to be non-toxic, have reasonable absorption and permeability, and exhibit a more favorable ADMET profile. Table 3 Drug-likeness properties of hits Parameters Bictegravir Cabotegravir Molecular weight 449.12 405.11 H-Bond acceptor (nHA) 8 8 H-Bond donor (nHD) 2 2 Rotatable bonds (nRot) 4 4 violation 0 0 TPSA 100.87 100.87 LogP 1.393 1.196 LogD 1.478 1.233 Caco-2 and MDCK permeability are established models for investigating in vivo and in vitro permeability screening. Compounds having a predicted Caco-2 value > -5.15 log cm/s are considered to have a proper Caco-2 permeability (Ferreira & Andricopulo, 2019 ), while those having an apparent permeability coefficient (Papp) > 20 x 10 − 6 cm/s are said to have MDCK permeability. Interestingly, bictegravir and cabotegravir were predicted to have Caco-2 values of -4.722 and − 4.705, respectively, and Papp > 20 x 10 − 6 cm/s. Implying that both drugs could passively diffuse into the systemic circulation efficiently. The P-glycoprotein (Pgp) is a promiscuous efflux transporter that primarily eliminates xenobiotics or toxins from the body (Koehn, 2021 ). Therefore, inhibition of Pgp could lead to delayed clearance of xenobiotics from the body (Callaghan et al., 2014 ; Mealey & Fidel, 2015 ). Accordingly, Table 4 . below indicates that bictegravir and cabotegravir would not interfere with toxins clearance from the body with a Pgp inhibitory value < 0.3. Another important absorption parameter evaluated was the HIA (Human Intestinal Absorption), given its overall significance in drug efficacy. A drug is considered poorly absorbed if it has an absorbance of < 30%. Hence, the compound is classified as being HIA positive (+). However, bictegravir and cabotegravir were classified as HIA negative (-), indicating an absorbance value above 30% and excellent intestinal absorption. Also, the bictegravir and cabotegravir have been predicted to have no substantial effects on the central nervous system signaling functions with a relatively low blood-brain barrier (BBB) value of 0.19 and 0.322, respectively. The human cytochrome P450 isozymes 1A2, 2C19, 2C9, 2D6, and 3A4 are the major group of metabolizing enzymes that shouldered the metabolism of about two-thirds of drugs in humans (Ogu & Maxa, 2017 ). Thus, drug-drug interactions may arise when a drug blocks or induces these proteins. Both hit molecules are non-inhibitors or non-substrates of all these isozymes except that they are substrates to CYP2C9. Demonstrating the impressive metabolic properties of these compounds. Further, bictegravir and cabotegravir have very low clearance values of 3.38 and 2.445, respectively. However, their half-life falls consistently within the acceptable range (0-0.3). The hERG (human ether-a-go-go-related gene) potassium channel regulates cardiac action and resting potential. Therefore, inhibiting the hERG channels could prolong QT intervals and increase ventricular arrhythmia risks (Lamothe et al., 2016 ; Vandenberg et al., 2012 ). The obstruction of hERG channels could result in fainting, palpitations, cardiac arrest, or sudden death. Our screening reveals that both hits don't obstruct hERG channels, suggesting they have no negative impact on cardiac health. Also, the predicted carcinogenicity and AMES Toxicity values of bictegravir and cabotegravir showed that both hits would have no serious effects on human health. The most important step in evaluating any drug candidate's safety is determining its acute toxicity in mammals. In this regard, bictegravir and cabotegravir have been predicted to display excellent safety profiles. However, both have a probability of being toxic to the liver. Table 4 ADMET profile of hits and reference compound ADMET Parameters Bictegravir Cabotegravir Reference hERG blockers -- -- Carcinogenicity - -- AMES Toxicity - -- Rat Oral Acute Toxicity -- -- Clearance (ml/min/kg) 3.308 2.445 ≥ 5 T ½ -- -- BBB penetration -- - HIA -- -- Pgp-inhibitor -- -- Caco-2 (log cm/s) -4.722 -4.705 ≥ − 5.15 MDCK (cm/s) 1.6e-05 1.1e-05 > 20 x 10 − 6 hERG = Human ether-a-go-go-related gene; MDCK = Madin-Darby Canine Kidney Cells; HIA = Human intestinal absorption; Pgp = P-glycoprotein; Caco-2 = Colon adenocarcinoma cell line; T ½ = Half-life Prediction keys: 0-0.3 (--/excellent), 0.3–0.7 (-/medium), and 0.7-1.0 (++/poor) 3.4 Molecular Dynamics Simulation Analysis In other to investigate the structural integrity of the integrase inhibitors in the binding pocket of MCL-1 protein (6OQB), the atomistic MD simulation trajectories of the protein-ligand complex systems were aligned to those of the unliganded system to assess various thermodynamic parameters discussed below. 3.4.1 Root Mean Square Deviation (RMSD) The structural stability of the unliganded MCL-1 (6OQB) and MCL-1 complexed with bictegravir, cabotegravir, and reference compound (AMG176) and their progression within the aqueous milieu of the protein active site during the simulation was monitored using RMSD. As shown in Fig. 3, the four complex systems experienced fluctuation at the start of the simulation run. However, the bictegravir-MCL-1 complex became stable from around 30 ns until the completion of the simulation period with an average RMSD value of 3.457 Å. Conversely, the unliganded MCL-1 (apoprotein), cabotegravir-MCL-1, and AMG176-MCL1 complex attain a convergent system at about 75 ns through to the simulation endpoint with average RMSD values of 3.422, 2.909, and 3.131 Å, respectively. The binding of bictegravir considerably reduced fluctuation in the bictegravir-MCL-1 complex, indicating a more compact structure upon binding to the MCL-1 protein. An RMSD value closer to that of the apoprotein signals a stabilized system (Liu et al., 2017 ). Therefore, bictegravir demonstrates greater stability within the binding pocket of MCL-1 among the studied complex. Figure 3. RMSD plots of MCL-1 (6OQB) complexed to hit molecules and reference compound. 3.4.2 Root Mean Square Fluctuation (RMSF) RMSF provides insight into the flexibility of the amino acid residues of a macromolecular structure. The minimum and maximum fluctuation for the unliganded MCL-1, bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complex are 0.5030–8.324, 0.4100–4.353, 0.4740-7.000, and 0.4610–8.057 Å, respectively (Fig. 4). For the four complex systems, the utmost fluctuation was seen in the Gly326 residues, which do not participate in interacting with the ligands. Further analysis of each biomolecular complex system revealed that the active site residues of the unliganded MCL-1 system have minimal fluctuation (< 2 Å), suggesting a stable binding pocket to propagate efficient interaction with the ligands. Also, the amino acid residues contributing to the bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complexes' structural stability have an RMSF value below 2 Å. The unliganded system and MCL-1 complexed with bictegravir, cabotegravir, and AMG176 have mean RMSF values of 1.272, 1.001, 1.151, and 1.094 Å, respectively. A smaller value of this thermodynamic parameter translates into higher complex system stability (Khoutoul et al., 2016 ). The four complex systems have substantially low RMSF values; however, the bictegravir-MCL-1 complex exhibits superior stability than other protein-ligand complexes. Figure 4. RMSF plots of MCL-1 (6OQB) complexed to hit molecules and reference compound. 3.4.3 Radius of Gyration (RoG) The biomolecular systems RoG was measured to predict the relative compactness of the MCL-1 protein (6OQB) upon ligand binding in the dynamic simulation environment. The equilibration of the RoG plot over the simulation time scale represents a stably folded complex system, while significant fluctuation in the RoG spectrum indicates a non-compact system (Khoutoul et al., 2016 ). The RoG plots computed from the MD simulation trajectories showed that the four complex systems exhibit steady progression with no significant structural shift away from their original structures (Fig. 5). The calculated average RoG values for the unliganded MCL-1, bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complex systems are 15.27, 15.13, 15.24, and 15.25 Å, respectively. Thus, the similar average RoG values suggest a well-folded and compacted system. Figure 5. RoG plots of MCL-1 (6OQB) complexed to hit molecules and reference compound. 3.4.4 Solvent Accessible Surface Area (SASA) Determining the extent of solvent accessible by the surface area of the unliganded MCL-1 complex and protein-ligand complexes helps predict the biomolecular structures' behavior in the hydrated intracellular environment. The computed average SASA values of the unliganded MCL-1, bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complexes are 8643, 8226, 8316, and 8333 Å, respectively. All the bound complexes interact with solvents with almost similar surface areas throughout the simulation (Fig. 6 ). Howbeit, the unliganded MCL-1 surface appears to be more accessible to solvent. 3.4.5 Hydrogen Bond Analysis (H-Bonds) Hydrogen bonds are an efficient molecular interaction contributing to receptor-ligand complexes stability (Chen et al., 2016 ). Hence, the higher the amount of hydrogen bonds formed in a protein-ligand complex, the more stable the interaction between them (Menéndez et al., 2016 ). To better determine the number of conventional H-Bonds propagating the complex system’s interaction, we examined the H-Bonds of the individual protein-ligand complexes factoring in the water molecules in the protein binding pocket. The mean number of H-Bonds calculated for the bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complexes throughout the simulation time scale are 0.2228, 0.1568, and 0.06893, respectively. Suggesting that the hit molecules have a significantly higher hydrogen bond number than the reference compound, which is in consonant with the molecular docking result. However, bictegravir has the highest number of H-bonds (Fig. 7 ). The result indicates that bictegravir and cabotegravir formed a more stable complex with MCL-1 than AMG176. The binding strength of bictegravir and cabotegravir in the MCL-1 protein (6OQB) active pocket was further investigated using the Molecular Mechanics Generalized Born Surface Area (MMGBSA) expanse. The computed average binding free energy (∆G Bind) for the bictegravir-MCL-1 and cabotegravir-MCL-1 complexes are − 61.52 and − 67.39 kcal/mol, respectively (Table 5 .). Establishing the strong binding affinity of the hit molecules for the target protein. Further, the protein dynamics were characterized using principal component analysis (David & Jacobs, 2014 ). The trajectory motions throughout the MD simulation time scale were observed and analyzed to determine the most relevant protein motions in the unliganded MCL-1, bictegravir-MCL-1, and cabotegravir-MCL-1 complex systems. The plots of protein (eigenvalues) against the eigenvector index (eigenmode) for the analyzed motion in all the complex systems showed stability (Figs. 8 , 9, and 10 ). The eigenvalues represent the fluctuations of the hyperspace eigenvector. Accordingly, the eigenvectors with the highest eigenvalues in MD simulation regulate the overall mobility of the protein target. In the studied systems, the utilized eigenvectors of the three complexes demonstrate dominant movements and higher eigenvalues over other eigenvectors (unliganded MCL-1: 39.5–75.2%; bictegravir-MCL-1: 29.6–69.1%; cabotegravir-MCL-1: 23.1–70.6%). The observed variations were analyzed and plotted in three principal components (PC1, PC2, and PC3). Interestingly, PC3 clusters had the least variability in all three biomolecular systems (unliganded MCL-1: 5.63%, bictegravir-MCL-1: 7.07%, and cabotegravir-MCL-1: 10.47%). Thus, indicating the most compact structure and stable conformation of the unliganded MCL-1, bictegravir-MCL-1, and cabotegravir-MCL-1 complex. Table 5 MMGBSA binding free energy of hit compounds Compounds dG 0 ns (kcal/mol) dG 100 ns (kcal/mol) dG Average (kcal/mol) Bictegravir -70.99 -52.04 -61.52 Cabotegravir -77.43 -57.34 -67.39 4. Conclusion MCL-1 is a central driver of tumor cell survival and drug resistance, making it a high-priority therapeutic target in various solid and hematological malignancies. Given MCL-1 prominence in the evasion of intrinsic apoptotic cell death in cancer cells, developing high-efficacy MCL-1 inhibitors has become a necessity. In this study, we identified bictegravir and cabotegravir as promising MCL-1 protein inhibitors. Beyond their high-affinity binding to MCL-1 hotspot residues, these drugs exhibit persuasive pharmacokinetic properties. In addition, throughout the 100 ns simulation run, these drugs showed stable binding interactions in the MCL-1 active pocket. Thus, our findings suggest that these integrase inhibitors could be repurposed for cancers overexpressing MCL-1 owing to their potent antagonistic activity against the protein. However, further studies involving experimental biological models are required to unravel their novel anticancer activity and ascertain their clinical efficacy in cancer treatment. Statements and Declaration Abbreviations A1 - BCL-2-related gene A1 ADMET - Absorption, Distribution, Metabolism, Excretion, and Toxicity ADT - AutoDockTools ALL - Acute lymphoblastic leukemia AML - Acute myeloid leukemia ATRA - All Trans Retinoic Acid BAD - BCL-2 associated agonist of cell death BAK - BCL-2 homologous antagonist killer BAX - BCL-2-associated X protein BCL-2 - B-cell Lymphoma 2 BCL-W - BCL-2-like protein 2 BCL-xL - BCL-2-like protein X BH - BCL-2 homology BID - BH3 interacting domain death agonist BIK - BCL-2 interacting killer BIM - BCL-2 interacting mediator of cell death BMF - BCL-2 modifying factor BOK - BCL-2 related ovarian killer Caco-2 - Colon adenocarcinoma cell line CASTp - Computed atlas of surface topography of proteins CYP - Cytochromes P450 Cyt C - Cytochrome C DR - Death receptor FasR - Fatty acid synthetase receptor FDA - Food and Drug Administration H-Bond - Hydrogen bonds HCT116 - Human colorectal carcinoma cell line hERG - Human ether-a-go-go-related gene HIA - Human intestinal absorption HRK - Harakiri/BCL-2 interacting protein LR5 - Lipinski’s rule of five MCL-1 - Myeloid cell leukemia-1 MD - Molecular dynamics MDCK - Madin-Darby Canine Kidney Cells MLL - Mixed-lineage leukemia MOMP - Mitochondria outer membrane permeabilization NCCN - National Comprehensive Cancer Network NSCLC - Non-small cell lung cancer Papp - Apparent permeability coefficient PDB - Protein Data Bank PDBQT - Protein Data Bank, Partial Charge, & Atom Type Pgp - P-glycoprotein PUMA - p53 upregulated modulator of apoptosis RCSB - Research Collaboratory for Structural Bioinformatics RMSD - Root mean square deviation RMSF - Root mean square fluctuation RoG - Radius of gyration SASA - Solvent accessible surface area SDF - Structure data file TNFR - Tumor necrosis factor receptor TPSA - Topological polar surface area Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and material The data supporting the study’s findings are available within the article. Competing interests The authors declare no financial or non-financial competing interests. Funding Not applicable. Authors’ contributions LOA — Conceptualization, Methodology, Data curation, Writing — original draft, Writing — review and editing. FNA — Methodology, Data curation, Writing — review and editing. OMO — Methodology, Data curation, Writing—review and editing. ROA — Methodology, Data curation, Writing—review and editing. OAA — Writing—review and editing. SFH — Conceptualization, Supervision, Writing—review and editing. MAG — Conceptualization, Supervision, Writing—review and editing. Acknowledgments Not applicable. References A Study of Venetoclax and AMG 176 in Patients With Relapsed/Refractory Hematologic Malignancies - Full Text View - ClinicalTrials.gov . (n.d.). 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Elevated expression of mcl-1 inhibits apoptosis and predicts poor prognosis in patients with surgically resected non-small cell lung cancer. Diagnostic Pathology , 14 (1), 1–9. https://doi.org/10.1186/S13000-019-0884-3/TABLES/2 Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., & Cao, D. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research , 49 (W1), W5–W14. https://doi.org/10.1093/NAR/GKAB255 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4412066","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305052283,"identity":"8f45d5b0-5f85-4d01-aa37-aff0bd7a177a","order_by":0,"name":"Lateef O. Anifowose","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPhCRYANmMz4AEjx8hLSwgbWkgdnMBiAtbERpYYBoYZNAiODTwt787MGDBJvEfv7Dxyq/5tjJsDEwP3x0A58WnmPmBgkJaYkzZ6Sl3Zbdlgx0GJuxcQ4+LRIJZhKJPw7nbrjBY3ZbchszUAsPmzR+LenfJBISgFrOn/9WLLmtnhgtOWYQLQdy2Bg/bjtMhBaeM2VALWn1QL8YSzNuO87DxkzAL/zs7dskfyTYGPPzH3748ee2ant+9uaHj/FpQQHMPGCSWOUgwPiDFNWjYBSMglEwYgAA2GFCJXWKfYMAAAAASUVORK5CYII=","orcid":"","institution":"Egypt-Japan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Lateef","middleName":"O.","lastName":"Anifowose","suffix":""},{"id":305052285,"identity":"a3e16462-3759-4e2a-a7d3-577f52eb614b","order_by":1,"name":"Fikayo N. Adegboyega","email":"","orcid":"","institution":"Egypt-Japan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fikayo","middleName":"N.","lastName":"Adegboyega","suffix":""},{"id":305052286,"identity":"439edd97-36c9-4897-9624-482109476a5a","order_by":2,"name":"Oludare M. Ogunyemi","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Oludare","middleName":"M.","lastName":"Ogunyemi","suffix":""},{"id":305052287,"identity":"bc2f0fad-dde2-4800-8f44-21f707ddc8ae","order_by":3,"name":"Rukayat O. Akano","email":"","orcid":"","institution":"Ladoke Akintola University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Rukayat","middleName":"O.","lastName":"Akano","suffix":""},{"id":305052289,"identity":"2483dd3d-12c8-4c68-b2ed-f876c41342ff","order_by":4,"name":"Oluwatoyin A. Adeyemo-Salami","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Oluwatoyin","middleName":"A.","lastName":"Adeyemo-Salami","suffix":""},{"id":305052290,"identity":"6f3d7347-d057-4521-b1a8-5667d23092ec","order_by":5,"name":"Sherif F. Hammad","email":"","orcid":"","institution":"Egypt-Japan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sherif","middleName":"F.","lastName":"Hammad","suffix":""},{"id":305052291,"identity":"f99f88df-1773-483a-9609-f227c3f1db7a","order_by":6,"name":"Mohamed A. Ghazy","email":"","orcid":"","institution":"Egypt-Japan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"A.","lastName":"Ghazy","suffix":""}],"badges":[],"createdAt":"2024-05-13 09:10:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4412066/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4412066/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56917076,"identity":"479e9db6-3783-46b5-9a92-9467db6ecf3e","added_by":"auto","created_at":"2024-05-22 06:34:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93792,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/7e46d532c65284a4fe616278.png"},{"id":56917071,"identity":"1e5c266a-5329-452d-aaa6-78f7e168e0e0","added_by":"auto","created_at":"2024-05-22 06:34:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":502344,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular interaction of hits and reference compound with MCL-1 protein.\u003c/p\u003e\n\u003cp\u003e(a) Bictegravir (b) Cabotegravir (c) AMG176 (Reference compound)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/7122463744c005b455531c8a.png"},{"id":56917516,"identity":"6c4d3b05-cbb6-4b96-8770-be2ee871e4da","added_by":"auto","created_at":"2024-05-22 06:42:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79591,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/3ac59aadfa0cde2891795401.png"},{"id":56917866,"identity":"0fc98eca-2788-4a0f-a617-d829416af245","added_by":"auto","created_at":"2024-05-22 06:50:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160316,"visible":true,"origin":"","legend":"\u003cp\u003eRMSF plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/72c65e3a6d31fd73f322d269.png"},{"id":56917074,"identity":"e0cae1c8-bf5f-4c74-b096-71facce92c2c","added_by":"auto","created_at":"2024-05-22 06:34:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70164,"visible":true,"origin":"","legend":"\u003cp\u003eRoG plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/047fce23cdbeb4f9771422c2.png"},{"id":56918453,"identity":"c9d0b241-33e6-4186-b6a5-3e7146c74ae9","added_by":"auto","created_at":"2024-05-22 06:58:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76955,"visible":true,"origin":"","legend":"\u003cp\u003eSASA plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/41d0ab42d412eb4022f9994c.png"},{"id":56917519,"identity":"363848fc-4558-4401-be9b-0d7cf5855cd8","added_by":"auto","created_at":"2024-05-22 06:42:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54360,"visible":true,"origin":"","legend":"\u003cp\u003eH-Bond plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/50a5087127cac5144e67ee8d.png"},{"id":56917080,"identity":"c50ad110-85b5-47c5-8706-9ebbee90f5b7","added_by":"auto","created_at":"2024-05-22 06:34:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":46044,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of eigenvalue against variance percentage (unliganded MCL-1).\u003cstrong\u003e\u003cbr\u003e\n \u003c/strong\u003eBlue = most significant mobility; White = intermediate movement; Red = less flexibility\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/8b1b8b94f692f40a7b0ffb2a.png"},{"id":56917520,"identity":"a5149786-e73b-4a7b-8754-08cde388ea05","added_by":"auto","created_at":"2024-05-22 06:42:30","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":83558,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of eigenvalue against variance percentage (bictegravir-MCL-1). \u0026nbsp;\u003cbr\u003e\n Blue = most significant mobility; White = intermediate movement; Red = less flexibility\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/42be7876762607ee60071677.png"},{"id":56917078,"identity":"bcf037dd-91e0-472a-807d-19c2912f8f5b","added_by":"auto","created_at":"2024-05-22 06:34:30","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":103880,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of eigenvalue against variance percentage (cabotegravir-MCL-1). \u0026nbsp;\u003cbr\u003e\n Blue = most significant mobility; White = intermediate movement; Red = less flexibility\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/06777a6b50a3831d461ff7d2.png"},{"id":77283266,"identity":"e058cb5f-3f83-4c37-ae5c-bb83e611d911","added_by":"auto","created_at":"2025-02-27 04:46:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2627677,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4412066/v1/49554062-8e76-4491-bf32-1d31a9e2f949.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disruption of Oncogenic MCL-1-BAX/BAK Interaction Using Integrase Inhibitors: Insights from a Molecular Docking and Dynamic Exploration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eApoptosis is a tightly regulated physiological cell death program that plays an indispensable role in the maintenance of normal tissue homeostasis and development throughout life (Galluzzi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is a highly conserved defense mechanism for efficiently eliminating unwanted, damaged, potentially dangerous, or transformed cells in multicellular organisms (Czabotar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Hence, the deregulation of this physiological process is considered one of the significant life-threatening and aggressive phenomena in pathological conditions such as cancer (Czabotar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hanahan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The principal signaling pathways of apoptosis in mammalian cells are the extrinsic (death receptor) and intrinsic (mitochondrial) pathways (Hanahan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hanahan \u0026amp; Weinberg, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The extrinsic apoptotic pathway is mediated via extracellular ligand binding to death receptors on cell surfaces such as FasR (fatty acid synthetase receptor), DR3/DR4/DR5 (death receptor 3/4/5), and TNFR (tumor necrosis factor receptor) (Brenner et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). On the contrary, the intrinsic apoptotic pathway is prominently activated by endogenous and exogenous cues (e.g., oxidative stress, oncogene activation, DNA damage, radiotherapy, etc.) and is strictly regulated by the B-cell lymphoma 2 (BCL-2) proteins (Green, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe BCL-2 protein family members have at least one BCL-2 homologous domains (BH1-4) and can be broadly grouped into proapoptotic effectors (BAX, BAK, and BOK), pro-survival/antiapoptotic proteins (BCL-2, BCL-W, BCL-xL, A1, and MCL-1), and BH3-only proteins (which can be either an activator [BIM, BID, and PUMA], or sensitizer [BAD, BIK, NOXA, BMF, and HRK]) (Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This protein family is an essential component of the mitochondria apoptotic pathway. Hence, the precise equilibrium between the anti- and proapoptotic proteins is critical to a cell\u0026rsquo;s decision between death and life (Bolomsky et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Under normal cellular circumstances, antiapoptotic proteins bind and sequester apoptotic effectors and BH-3-only activators, thereby blocking the dimerization of BAK and BAX and ensuring cell survival (Bolomsky et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, upon stimulation by a stress signal, either or both of the following events ensue. BH-3-only proteins (activators and sensitizers) can directly interact with antiapoptotic proteins, thus facilitating the release of the sequestered BAK and BAX, or free BH-3-only activator proteins can directly bind to and switch on BAK and BAX (Bolomsky et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Quinn et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consequently, this results in the homo-oligomerization of BAK and BAX, which is accompanied by pore formation on the mitochondria membrane (a process described as MOMP \u0026ndash; mitochondria outer membrane permeabilization), cytochrome C release into the cytoplasm, apoptosome formation, caspase cascade activation, and finally cell death (Roy et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn cancerous cells, the relative ratios of BCL-2 protein members in the mitochondria membrane are more inclined towards antiapoptotic proteins than apoptotic effectors, making the former a high-priority therapeutic target in various malignancies (Delbridge \u0026amp; Strasser, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Myeloid cell leukemia-1 (MCL-1) is a prominent representative of the BCL-2 antiapoptotic protein family (H. Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is crucial in many cells' development and survival, including B- and T- lymphocytes, cardiomyocytes, and the nervous system (Fogarty et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Opferman et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; X. Wang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Three spliced variants of MCL-1 (MCL-1S, MCL-1ES, and MCL-1L) exist, each having a distinct role in apoptosis (H. Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The most extended variant is MCL-1L, encoded by the exon I-III genes (H. Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It acts as an antiapoptotic factor and is traditionally regarded as MCL-1 (H. Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). MCL-1 has three putative BH domains, and its larger size of about 350 residues distinguishes it from its pro-survival relatives (Thomas et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The overamplification of the MCL-1 gene is a typical aberration protecting various hematological and solid tumors from apoptosis (Belmar \u0026amp; Fesik, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is understood to contribute to survival in NSCLC (Wen et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), liver cancers (Akgul, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), melanoma (Boisvert-Adamo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), AML (acute myeloid leukemia), lymphoma, and plasma cell myeloma cell lines (Phillips et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Slomp \u0026amp; Peperzak, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tron et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Further, MCL-1 gene (1q21.2) amplification has been touted as a resistance strategy against a plethora of established anticancer therapies, including resistance to lapatinib in HCT116 cells (Martin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), resistance to prednisone in mixed lineage leukemia-rearranged infant ALL (Stam et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), cisplatin-resistant in ovarian carcinoma cells (Simonin et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), radiotherapy, and BCL-2/BCL-xL targeting drugs (Hird \u0026amp; Tron, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shahar \u0026amp; Larisch, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, from a clinical standpoint, MCL-1 is an essential molecular target in cancer therapy.\u003c/p\u003e \u003cp\u003eDue to their genetically elevated levels in human cancers (Beroukhim et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as well as numerous implications in promoting cancer survival and drug resistance, a few compounds (S64315 [NCT03672695], AZD5991 [NCT03218683], PRT1419 [NCT04543305, NCT04837677], and AMG176 [NCT02675452]) that target MCL-1 are currently undergoing clinical evaluation (ClinicalTrials.Gov). Heretofore, none has been authorized for use in cancer treatment. Emphasizing the need to increase the momentum in our search for potent, safe, and selective MCL-1 inhibitors that could be used either as a single regime or in combination therapy. Developing novel oncological drugs is an intense, time- and money-consuming process with high developmental risk involving comprehensive pre-clinical and clinical studies (Turanli et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, exploring the anticancer benefits of approved non-cancer drugs for targeted therapy in cancer represents an advantageous approach over the conventional de novo drug discovery due to its cost-effectiveness, reduced development time, and lower risks for cancer patients (Ohmoto \u0026amp; Fuji, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thalidomide, arsenic trioxide, and ATRA (all-trans retinoic acid) are best-known examples of FDA-approved repurposed drugs considered as standard treatment in the National Comprehensive Cancer Network (NCCN) guidelines (Gonzalez-Fierro \u0026amp; Due\u0026ntilde;as-Gonz\u0026aacute;lez, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Of late, molecular docking has become a valuable tool in uncovering novel activity or targets for established drugs (Pinzi \u0026amp; Rastelli, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Herein, we screened thirty-one approved antiviral medicines for potential antagonistic activity against MCL-1 using a virtual computing technique.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ligand search and preparation for docking\u003c/h2\u003e \u003cp\u003eWe searched for antiviral drugs on the DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and thirty-one (31) antiviral drugs currently in use in clinical settings were randomly selected. The three-dimensional (3D) SDF (structure data file) conformers of these ligands and the reference drug (AMG176) were obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"http://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and PyRx OpenBabel was used to minimize energy and convert the ligands to AutoDock Ligand format (PDBQT) (Dallakyan \u0026amp; Olson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Protein preparation and active site determination\u003c/h2\u003e \u003cp\u003eThe pro-survival MCL-1 protein (6OQB) coordinate was recruited from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB - \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the protein repair and analysis server (PRAS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.protein-science.com/\u003c/span\u003e\u003cspan address=\"https://www.protein-science.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to add missing residues, polar hydrogen atoms, and delete water molecules (Nnyigide et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The BIOVIA Discovery Studio (BIOVIA, Dassault Systѐmes, Visualizer, v21.1.0.20298, San Diego: Dassault Systѐmes, 2021) was used to remove co-crystallized compound that could impact virtual screening, and the protein active site amino acid residues were detected using Computed Atlas of Surface Topography of proteins (CASTp) online web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sts.bioe.uic.edu/\u003c/span\u003e\u003cspan address=\"http://sts.bioe.uic.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Tian et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Accordingly, these amino acid residues were noted and used for site-specific docking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Docking validation (Redocking)\u003c/h2\u003e \u003cp\u003ePrior to our molecular docking experiment, our virtual screening software (PyRx) was validated to measure its ability to reproduce the ligand conformation in the crystallographic structure of 6OQB. Briefly, the bound ligand (N0J/AM-8621) was separated from the protein using BIOVIA Discovery Studio (BIOVIA, Dassault Systѐmes, Visualizer, v21.1.0.20298, San Diego: Dassault Systѐmes, 2021), and saved as a PDB file. The PyRx virtual screening tool was then used to redock the co-crystallized ligand back into the active pocket of the protein (6OQB), as demonstrated in our previous study (Anifowose et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). And the RSMD (Root Mean Square Deviation) value was computed using MGLTools (ADT 1.5.7 software) (Sanner, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular docking and post-docking analysis\u003c/h2\u003e \u003cp\u003eThe molecular docking experiment was run using the PyRx (0.8) virtual screening software (Dallakyan \u0026amp; Olson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Accordingly, the receptor grid box was adjusted to enclose the defined active site residues while maintaining the default grid box dimension (25x25x25) and exhaustiveness (8) throughout the docking procedure. Further, post-docking analysis was carried out to investigate the protein-ligand interaction using BIOVIA Discovery Studio (BIOVIA, Dassault Systѐmes, Visualizer, v21.1.0.20298, San Diego: Dassault Systѐmes, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Physicochemical properties and ADMETox evaluation\u003c/h2\u003e \u003cp\u003eThe ADMET properties of the top two hits were assessed using ADMETlab 2.0 webserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://admetmesh.scbdd.com/\u003c/span\u003e\u003cspan address=\"https://admetmesh.scbdd.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Xiong et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Diverse parameters in line with pharmacokinetics and toxicological endpoints were examined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Molecular dynamics simulation\u003c/h2\u003e \u003cp\u003eDesmond software from Schr\u0026ouml;dinger LLC (Bowers et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was employed for the 100 nanoseconds MD simulation of the receptor-ligand complexes. By incorporating Newton\u0026rsquo;s classical equation of motion, we investigated the structural integrity of the protein and protein-ligand complexes in a physiological environment over time (Hildebrand et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The system builder tool was used to prepare the complex systems for simulation. The TIP3P (Transferable intermolecular potential 3 points) water model with an orthorhombic box and OPLS_2005 force field was adopted (Shivakumar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Further, counter ions were introduced to neutralize the system, and 0.15 M sodium chloride (NaCl) was added to mimic a physiological condition. The isothermal-isobaric ensemble (constant number of particles N, pressure P, and temperature T \u0026ndash; NPT) of the Nose\u0026ndash;Hoover thermostat (Jorgensen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and Martyna\u0026ndash;Tobias\u0026ndash;Klein barostat was deployed to maintain the system\u0026rsquo;s temperature and pressure at 310 K and 1 bar, respectively. The trajectory data collection was set at 100 picoseconds (ps) intervals, and the model system was relaxed before the simulation experiment. Accordingly, the spectrum of various thermodynamic parameters, including root mean square deviation, root mean square fluctuation, the radius of gyration (RoG), solvent accessible surface area, and the number of hydrogen bonds, was plotted as a time frame function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Binding free energy determination and principal component analysis\u003c/h2\u003e \u003cp\u003eThe prime MMGBSA (molecular mechanics generalized Born surface area) module of Maestro 12.8 (Schr\u0026ouml;dinger Release 2021-2: Maestro, Schr\u0026ouml;dinger, LLC, New York, NY, 2021) was used to estimate the binding free energy change (∆G Bind) of the hit molecules before (0 ns) and after (100 ns) MD simulation. The VSGB solvent model, OPLS_2005 force field, and rotamer search techniques were adopted to calculate the complex systems' total free energy of binding. The principal component analysis (PCA) was performed using the Bio3D R package (Grant et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The R language script was used to compute the PCA from the Desmond MD trajectories (Kitao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Palma \u0026amp; Pierdominici-Sottile, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.1 Molecular docking validation\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.2 Molecular docking analysis\u003c/h2\u003e\n \u003cp\u003eAfter validating the screening protocol, the antiviral drugs and the reference compound (AMG176) were docked in the active pocket of MCL-1 to investigate their binding affinity for the protein target. The clinical inhibitor (AMG176) has a binding energy of -7.7 kcal/mol, and this value was set as the cut-off point to identify potential hits that could target MCL-1 hydrophobic BH-3 binding grooves with high affinity. A lower binding energy corresponds to a higher binding affinity of a ligand for the protein target (Simon et al., \u003cspan\u003e2017\u003c/span\u003e). Thus, ligands having a binding energy superior to the reference compound were considered hits (Table \u003cspan\u003e1\u003c/span\u003e). Several antiviral drugs displayed binding energy exceeding the reference compound\u0026apos;s (AMG176). However, the small-molecule integrase strand transfer inhibitors (bictegravir and cabotegravir) exhibiting a greater affinity for MCL-1 protein were considered for the molecular dynamic simulation study.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBinding energy of promising MCL-1 inhibitors and reference compound\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePubChem ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding affinity (kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e90311989\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBictegravir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-9.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e54713659\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCabotegravir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-9.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e11527519\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElsulfavirine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-8.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9604654\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePradefovir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-8.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e491941\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePritelivir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-8.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e71345\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePirodavir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-7.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5625\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelavirdine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-7.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e118910268\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAMG176 (reference compound)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-7.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDeveloping effective MCL-1 inhibitors has proven difficult (Wan et al., \u003cspan\u003e2018\u003c/span\u003e). The large size and high lipophilicity of previously identified MCL-1 putative inhibitors have resulted in discouraging drug-like properties, including poor pharmacokinetic profile, limited cell membrane permeability, and lack of specificity (Arnold et al., \u003cspan\u003e2008\u003c/span\u003e; Mohammad et al., \u003cspan\u003e2007\u003c/span\u003e; Oliver et al., \u003cspan\u003e2004\u003c/span\u003e). Fortunately, several compounds with demonstrated efficacy in preclinical studies have progressed to clinical trials in recent years. Howbeit, to the best of our knowledge, none has been recommended for use in cancer treatment. Hence, the unmet therapeutic needs of cancer patients with genetically elevated levels of MCL-1 (Derenne et al., \u003cspan\u003e2002\u003c/span\u003e). MCL-1 hydrophobic BH-3 binding groove lacks structural plasticity (Caenepeel et al., \u003cspan\u003e2018\u003c/span\u003e). The key binding pocket on MCL-1 is shallow and rigid compared to the binding site of other antiapoptotic proteins (BCL-2 and BCL-xL), creating a fundamental challenge for scientists in developing MCL-1 inhibitors. The Arg263 and four hydrophobic pockets (P1-P4) are considered hotspots of the MCL-1 protein (Table \u003cspan\u003e2\u003c/span\u003e) (Denis et al., \u003cspan\u003e2020\u003c/span\u003e). However, nuclear magnetic resonance-based screening showed that inhibitors that bind with the region (P2 pocket) that form a large hydrophobic cavity upon ligand binding appear more potent (Belmar \u0026amp; Fesik, \u003cspan\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMCL-1 hotspots residues\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCL-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeu235\u003c/p\u003e\n \u003cp\u003eLeu246\u003c/p\u003e\n \u003cp\u003eVal249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMet231\u003c/p\u003e\n \u003cp\u003eVal253\u003c/p\u003e\n \u003cp\u003ePhe254\u003c/p\u003e\n \u003cp\u003eLeu267\u003c/p\u003e\n \u003cp\u003ePhe270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMet250\u003c/p\u003e\n \u003cp\u003eGly271\u003c/p\u003e\n \u003cp\u003eVal274\u003c/p\u003e\n \u003cp\u003eLeu290\u003c/p\u003e\n \u003cp\u003eIle294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHis224\u003c/p\u003e\n \u003cp\u003eAla227\u003c/p\u003e\n \u003cp\u003ePhe228\u003c/p\u003e\n \u003cp\u003eThr266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVal216\u003c/p\u003e\n \u003cp\u003eVal220\u003c/p\u003e\n \u003cp\u003eVal265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eA closer look into the binding mode of bictegravir and cabotegravir with the hydrophobic BH-3 binding grooves of MCL-1 protein revealed interesting protein-ligand interactions, as summarized in Table \u003cspan\u003e3\u003c/span\u003e. Bictegravir binds mainly in the P1 and P2 pockets of MCL-1 protein through pi-interactions (Leu246, Val253, Arg263, Val274, Met250, and Phe270), carbon hydrogen (Gly271), and halogen (Leu267) bond, and form a conventional hydrogen bond with Arg263 (Fig. \u003cspan\u003e2\u003c/span\u003ea). On the other hand, Fig. \u003cspan\u003e2\u003c/span\u003eb shows that cabotegravir primarily contacts the P1, P2, and P3 pockets via pi-interactions (Leu 246, Val253, Met250, and Phe270) and carbon-hydrogen bonds (Thr266 and Gly271), while also forming two conventional hydrogen bonds with Arg263. However, the reference compound (AMG176), specifically binds to residues in the P2 and P3 pockets mainly through pi-interactions (Ala227, Phe228, Met231, Val253, Leu267, and Phe270) and carbon hydrogen bond (His224) (Fig. \u003cspan\u003e2\u003c/span\u003ec). But no hydrogen bond was observed between AMG176 and Arg263. The filling of the P2 binding region and additional hydrogen bond with Arg263 allows a tighter interaction of bictegravir and cabotegravir with MCL-1 protein, thus increasing their inhibitory activity. Highlighting the strong potential of these integrase inhibitors to disrupt the protein-protein (MCL-1-BAX/BAK) interactions promoting cancer cell survival and drug resistance.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMolecular interactions of hits and reference compound\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInteracting residues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConventional H-bond\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBictegravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeu246, Met250, Val253, Leu267, Phe270, Gly271, and Val274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCabotegravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeu 246, Met250, Val253, Thr266, Phe270, and Gly271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMG176\u003c/p\u003e\n \u003cp\u003e(Reference compound)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHis224, Ala227, Phe228, Met231, Val253, Leu267, and Phe270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.3 Physicochemical properties and ADMETox evaluation\u003c/h2\u003e\n \u003cp\u003eHerein, we further appraise the pharmacokinetic properties of the hits that earned them the U.S. FDA (Food and Drug Administration) approvals in 2018 (Bictegravir) and 2021 (Cabotegravir) as monotherapy in combination with other antiretroviral agents in HIV infection. Importantly, to gain insight into their specific ADME behavior and toxicological parameters as a single agent early in the drug discovery pipeline. According to Lipinski\u0026apos;s rule of five (LR5), a small-molecule drug should not flout more than one of the under-listed criteria: molecular weight\u0026thinsp;\u0026lt;\u0026thinsp;500 Da, hydrogen bond acceptor (N or O)\u0026thinsp;\u0026le;\u0026thinsp;10, an octanol-water partition coefficient (log P)\u0026thinsp;\u0026le;\u0026thinsp;5, and hydrogen bond donor (N.H. or O.H.)\u0026thinsp;\u0026le;\u0026thinsp;5 (Lipinski, \u003cspan\u003e2004\u003c/span\u003e). Interestingly, from Table \u003cspan\u003e3\u003c/span\u003e. below, it could be inferred that bictegravir and cabotegravir violated none of the rules, hence their conformance to the LR5 in addition to the Pfizer\u0026apos;s (logP\u0026thinsp;\u0026gt;\u0026thinsp;3; TPSA\u0026thinsp;\u0026lt;\u0026thinsp;75) and Golden Triangle (200\u0026thinsp;\u0026le;\u0026thinsp;M.W. \u0026le; 50; -2\u0026thinsp;\u0026le;\u0026thinsp;logD\u0026thinsp;\u0026le;\u0026thinsp;5) rules. Suggesting that bictegravir and cabotegravir are expected to be non-toxic, have reasonable absorption and permeability, and exhibit a more favorable ADMET profile.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDrug-likeness properties of hits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBictegravir\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCabotegravir\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e449.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e405.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-Bond acceptor (nHA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-Bond donor (nHD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRotatable bonds (nRot)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eviolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCaco-2 and MDCK permeability are established models for investigating \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e permeability screening. Compounds having a predicted Caco-2 value \u0026gt; -5.15 log cm/s are considered to have a proper Caco-2 permeability (Ferreira \u0026amp; Andricopulo, \u003cspan\u003e2019\u003c/span\u003e), while those having an apparent permeability coefficient (Papp)\u0026thinsp;\u0026gt;\u0026thinsp;20 x 10\u0026thinsp;\u0026minus;\u0026thinsp;6 cm/s are said to have MDCK permeability. Interestingly, bictegravir and cabotegravir were predicted to have Caco-2 values of -4.722 and \u0026minus;\u0026thinsp;4.705, respectively, and Papp\u0026thinsp;\u0026gt;\u0026thinsp;20 x 10\u0026thinsp;\u0026minus;\u0026thinsp;6 cm/s. Implying that both drugs could passively diffuse into the systemic circulation efficiently. The P-glycoprotein (Pgp) is a promiscuous efflux transporter that primarily eliminates xenobiotics or toxins from the body (Koehn, \u003cspan\u003e2021\u003c/span\u003e). Therefore, inhibition of Pgp could lead to delayed clearance of xenobiotics from the body (Callaghan et al., \u003cspan\u003e2014\u003c/span\u003e; Mealey \u0026amp; Fidel, \u003cspan\u003e2015\u003c/span\u003e). Accordingly, Table \u003cspan\u003e4\u003c/span\u003e. below indicates that bictegravir and cabotegravir would not interfere with toxins clearance from the body with a Pgp inhibitory value\u0026thinsp;\u0026lt;\u0026thinsp;0.3. Another important absorption parameter evaluated was the HIA (Human Intestinal Absorption), given its overall significance in drug efficacy. A drug is considered poorly absorbed if it has an absorbance of \u0026lt;\u0026thinsp;30%. Hence, the compound is classified as being HIA positive (+). However, bictegravir and cabotegravir were classified as HIA negative (-), indicating an absorbance value above 30% and excellent intestinal absorption. Also, the bictegravir and cabotegravir have been predicted to have no substantial effects on the central nervous system signaling functions with a relatively low blood-brain barrier (BBB) value of 0.19 and 0.322, respectively. The human cytochrome P450 isozymes 1A2, 2C19, 2C9, 2D6, and 3A4 are the major group of metabolizing enzymes that shouldered the metabolism of about two-thirds of drugs in humans (Ogu \u0026amp; Maxa, \u003cspan\u003e2017\u003c/span\u003e). Thus, drug-drug interactions may arise when a drug blocks or induces these proteins. Both hit molecules are non-inhibitors or non-substrates of all these isozymes except that they are substrates to CYP2C9. Demonstrating the impressive metabolic properties of these compounds. Further, bictegravir and cabotegravir have very low clearance values of 3.38 and 2.445, respectively. However, their half-life falls consistently within the acceptable range (0-0.3). The hERG (human ether-a-go-go-related gene) potassium channel regulates cardiac action and resting potential. Therefore, inhibiting the hERG channels could prolong QT intervals and increase ventricular arrhythmia risks (Lamothe et al., \u003cspan\u003e2016\u003c/span\u003e; Vandenberg et al., \u003cspan\u003e2012\u003c/span\u003e). The obstruction of hERG channels could result in fainting, palpitations, cardiac arrest, or sudden death. Our screening reveals that both hits don\u0026apos;t obstruct hERG channels, suggesting they have no negative impact on cardiac health. Also, the predicted carcinogenicity and AMES Toxicity values of bictegravir and cabotegravir showed that both hits would have no serious effects on human health. The most important step in evaluating any drug candidate\u0026apos;s safety is determining its acute toxicity in mammals. In this regard, bictegravir and cabotegravir have been predicted to display excellent safety profiles. However, both have a probability of being toxic to the liver.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eADMET profile of hits and reference compound\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADMET Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBictegravir\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCabotegravir\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehERG blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarcinogenicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMES Toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRat Oral Acute Toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClearance (ml/min/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT \u0026frac12;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB penetration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePgp-inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaco-2 (log cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge; \u0026minus;\u0026thinsp;5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDCK (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;20 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ehERG\u0026thinsp;=\u0026thinsp;Human ether-a-go-go-related gene; MDCK\u0026thinsp;=\u0026thinsp;Madin-Darby Canine Kidney Cells; HIA\u0026thinsp;=\u0026thinsp;Human intestinal absorption; Pgp\u0026thinsp;=\u0026thinsp;P-glycoprotein; Caco-2\u0026thinsp;=\u0026thinsp;Colon adenocarcinoma cell line; T \u0026frac12; = Half-life\u003c/p\u003e\n \u003cp\u003ePrediction keys: 0-0.3 (--/excellent), 0.3\u0026ndash;0.7 (-/medium), and 0.7-1.0 (++/poor)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.4 Molecular Dynamics Simulation Analysis\u003c/h2\u003e\n \u003cp\u003eIn other to investigate the structural integrity of the integrase inhibitors in the binding pocket of MCL-1 protein (6OQB), the atomistic MD simulation trajectories of the protein-ligand complex systems were aligned to those of the unliganded system to assess various thermodynamic parameters discussed below.\u003c/p\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.4.1 Root Mean Square Deviation (RMSD)\u003c/h2\u003e\n \u003cp\u003eThe structural stability of the unliganded MCL-1 (6OQB) and MCL-1 complexed with bictegravir, cabotegravir, and reference compound (AMG176) and their progression within the aqueous milieu of the protein active site during the simulation was monitored using RMSD. As shown in Fig. 3, the four complex systems experienced fluctuation at the start of the simulation run. However, the bictegravir-MCL-1 complex became stable from around 30 ns until the completion of the simulation period with an average RMSD value of 3.457 \u0026Aring;. Conversely, the unliganded MCL-1 (apoprotein), cabotegravir-MCL-1, and AMG176-MCL1 complex attain a convergent system at about 75 ns through to the simulation endpoint with average RMSD values of 3.422, 2.909, and 3.131 \u0026Aring;, respectively. The binding of bictegravir considerably reduced fluctuation in the bictegravir-MCL-1 complex, indicating a more compact structure upon binding to the MCL-1 protein. An RMSD value closer to that of the apoprotein signals a stabilized system (Liu et al., \u003cspan\u003e2017\u003c/span\u003e). Therefore, bictegravir demonstrates greater stability within the binding pocket of MCL-1 among the studied complex.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 3.\u003c/strong\u003e RMSD plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.4.2 Root Mean Square Fluctuation (RMSF)\u003c/h2\u003e\n \u003cp\u003eRMSF provides insight into the flexibility of the amino acid residues of a macromolecular structure. The minimum and maximum fluctuation for the unliganded MCL-1, bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complex are 0.5030\u0026ndash;8.324, 0.4100\u0026ndash;4.353, 0.4740-7.000, and 0.4610\u0026ndash;8.057 \u0026Aring;, respectively (Fig. 4). For the four complex systems, the utmost fluctuation was seen in the Gly326 residues, which do not participate in interacting with the ligands. Further analysis of each biomolecular complex system revealed that the active site residues of the unliganded MCL-1 system have minimal fluctuation (\u0026lt;\u0026thinsp;2 \u0026Aring;), suggesting a stable binding pocket to propagate efficient interaction with the ligands. Also, the amino acid residues contributing to the bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complexes\u0026apos; structural stability have an RMSF value below 2 \u0026Aring;. The unliganded system and MCL-1 complexed with bictegravir, cabotegravir, and AMG176 have mean RMSF values of 1.272, 1.001, 1.151, and 1.094 \u0026Aring;, respectively. A smaller value of this thermodynamic parameter translates into higher complex system stability (Khoutoul et al., \u003cspan\u003e2016\u003c/span\u003e). The four complex systems have substantially low RMSF values; however, the bictegravir-MCL-1 complex exhibits superior stability than other protein-ligand complexes.\u003cstrong\u003eFigure 4.\u003c/strong\u003e RMSF plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.4.3 Radius of Gyration (RoG)\u003c/h2\u003e\n \u003cp\u003eThe biomolecular systems RoG was measured to predict the relative compactness of the MCL-1 protein (6OQB) upon ligand binding in the dynamic simulation environment. The equilibration of the RoG plot over the simulation time scale represents a stably folded complex system, while significant fluctuation in the RoG spectrum indicates a non-compact system (Khoutoul et al., \u003cspan\u003e2016\u003c/span\u003e). The RoG plots computed from the MD simulation trajectories showed that the four complex systems exhibit steady progression with no significant structural shift away from their original structures (Fig.\u0026nbsp;5). The calculated average RoG values for the unliganded MCL-1, bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complex systems are 15.27, 15.13, 15.24, and 15.25 \u0026Aring;, respectively. Thus, the similar average RoG values suggest a well-folded and compacted system.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e RoG plots of MCL-1 (6OQB) complexed to hit molecules and reference compound.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e3.4.4 Solvent Accessible Surface Area (SASA)\u003c/h2\u003e\n \u003cp\u003eDetermining the extent of solvent accessible by the surface area of the unliganded MCL-1 complex and protein-ligand complexes helps predict the biomolecular structures\u0026apos; behavior in the hydrated intracellular environment. The computed average SASA values of the unliganded MCL-1, bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complexes are 8643, 8226, 8316, and 8333 \u0026Aring;, respectively. All the bound complexes interact with solvents with almost similar surface areas throughout the simulation (Fig. \u003cspan\u003e6\u003c/span\u003e). Howbeit, the unliganded MCL-1 surface appears to be more accessible to solvent.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e3.4.5 Hydrogen Bond Analysis (H-Bonds)\u003c/h2\u003e\n \u003cp\u003eHydrogen bonds are an efficient molecular interaction contributing to receptor-ligand complexes stability (Chen et al., \u003cspan\u003e2016\u003c/span\u003e). Hence, the higher the amount of hydrogen bonds formed in a protein-ligand complex, the more stable the interaction between them (Men\u0026eacute;ndez et al., \u003cspan\u003e2016\u003c/span\u003e). To better determine the number of conventional H-Bonds propagating the complex system\u0026rsquo;s interaction, we examined the H-Bonds of the individual protein-ligand complexes factoring in the water molecules in the protein binding pocket. The mean number of H-Bonds calculated for the bictegravir-MCL-1, cabotegravir-MCL-1, and AMG176-MCL-1 complexes throughout the simulation time scale are 0.2228, 0.1568, and 0.06893, respectively. Suggesting that the hit molecules have a significantly higher hydrogen bond number than the reference compound, which is in consonant with the molecular docking result. However, bictegravir has the highest number of H-bonds (Fig. \u003cspan\u003e7\u003c/span\u003e). The result indicates that bictegravir and cabotegravir formed a more stable complex with MCL-1 than AMG176.\u003c/p\u003e\n \u003cp\u003eThe binding strength of bictegravir and cabotegravir in the MCL-1 protein (6OQB) active pocket was further investigated using the Molecular Mechanics Generalized Born Surface Area (MMGBSA) expanse. The computed average binding free energy (∆G Bind) for the bictegravir-MCL-1 and cabotegravir-MCL-1 complexes are \u0026minus;\u0026thinsp;61.52 and \u0026minus;\u0026thinsp;67.39 kcal/mol, respectively (Table \u003cspan\u003e5\u003c/span\u003e.). Establishing the strong binding affinity of the hit molecules for the target protein. Further, the protein dynamics were characterized using principal component analysis (David \u0026amp; Jacobs, \u003cspan\u003e2014\u003c/span\u003e). The trajectory motions throughout the MD simulation time scale were observed and analyzed to determine the most relevant protein motions in the unliganded MCL-1, bictegravir-MCL-1, and cabotegravir-MCL-1 complex systems. The plots of protein (eigenvalues) against the eigenvector index (eigenmode) for the analyzed motion in all the complex systems showed stability (Figs. \u003cspan\u003e8\u003c/span\u003e, 9, and \u003cspan\u003e10\u003c/span\u003e). The eigenvalues represent the fluctuations of the hyperspace eigenvector. Accordingly, the eigenvectors with the highest eigenvalues in MD simulation regulate the overall mobility of the protein target. In the studied systems, the utilized eigenvectors of the three complexes demonstrate dominant movements and higher eigenvalues over other eigenvectors (unliganded MCL-1: 39.5\u0026ndash;75.2%; bictegravir-MCL-1: 29.6\u0026ndash;69.1%; cabotegravir-MCL-1: 23.1\u0026ndash;70.6%). The observed variations were analyzed and plotted in three principal components (PC1, PC2, and PC3). Interestingly, PC3 clusters had the least variability in all three biomolecular systems (unliganded MCL-1: 5.63%, bictegravir-MCL-1: 7.07%, and cabotegravir-MCL-1: 10.47%). Thus, indicating the most compact structure and stable conformation of the unliganded MCL-1, bictegravir-MCL-1, and cabotegravir-MCL-1 complex.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMMGBSA binding free energy of hit compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompounds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edG 0 ns\u003c/p\u003e\n \u003cp\u003e(kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edG 100 ns (kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edG Average (kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBictegravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-70.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-52.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-61.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCabotegravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-77.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-57.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-67.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eMCL-1 is a central driver of tumor cell survival and drug resistance, making it a high-priority therapeutic target in various solid and hematological malignancies. Given MCL-1 prominence in the evasion of intrinsic apoptotic cell death in cancer cells, developing high-efficacy MCL-1 inhibitors has become a necessity. In this study, we identified bictegravir and cabotegravir as promising MCL-1 protein inhibitors. Beyond their high-affinity binding to MCL-1 hotspot residues, these drugs exhibit persuasive pharmacokinetic properties. In addition, throughout the 100 ns simulation run, these drugs showed stable binding interactions in the MCL-1 active pocket. Thus, our findings suggest that these integrase inhibitors could be repurposed for cancers overexpressing MCL-1 owing to their potent antagonistic activity against the protein. However, further studies involving experimental biological models are required to unravel their novel anticancer activity and ascertain their clinical efficacy in cancer treatment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatements and Declaration\u003c/b\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eA1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2-related gene A1\u003c/p\u003e\n\u003cp\u003eADMET\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Absorption, Distribution, Metabolism, Excretion, and Toxicity\u003c/p\u003e\n\u003cp\u003eADT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;AutoDockTools\u003c/p\u003e\n\u003cp\u003eALL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Acute lymphoblastic leukemia\u003c/p\u003e\n\u003cp\u003eAML\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Acute myeloid leukemia\u003c/p\u003e\n\u003cp\u003eATRA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;All Trans Retinoic Acid\u003c/p\u003e\n\u003cp\u003eBAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2 associated agonist of cell death\u003c/p\u003e\n\u003cp\u003eBAK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2 homologous antagonist killer\u003c/p\u003e\n\u003cp\u003eBAX\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2-associated X protein\u003c/p\u003e\n\u003cp\u003eBCL-2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;B-cell Lymphoma 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBCL-W\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2-like protein 2\u003c/p\u003e\n\u003cp\u003eBCL-xL\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2-like protein X\u003c/p\u003e\n\u003cp\u003eBH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2 homology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBID\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BH3 interacting domain death agonist\u003c/p\u003e\n\u003cp\u003eBIK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2 interacting killer\u003c/p\u003e\n\u003cp\u003eBIM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2 interacting mediator of cell death\u003c/p\u003e\n\u003cp\u003eBMF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2 modifying factor\u003c/p\u003e\n\u003cp\u003eBOK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL-2 related ovarian killer\u003c/p\u003e\n\u003cp\u003eCaco-2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Colon adenocarcinoma cell line\u003c/p\u003e\n\u003cp\u003eCASTp\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Computed atlas of surface topography of proteins\u003c/p\u003e\n\u003cp\u003eCYP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cytochromes P450\u003c/p\u003e\n\u003cp\u003eCyt C\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cytochrome C\u003c/p\u003e\n\u003cp\u003eDR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Death receptor\u003c/p\u003e\n\u003cp\u003eFasR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fatty acid synthetase receptor\u003c/p\u003e\n\u003cp\u003eFDA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Food and Drug Administration\u003c/p\u003e\n\u003cp\u003eH-Bond\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hydrogen bonds\u003c/p\u003e\n\u003cp\u003eHCT116\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human colorectal carcinoma cell line\u003c/p\u003e\n\u003cp\u003ehERG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human ether-a-go-go-related gene\u003c/p\u003e\n\u003cp\u003eHIA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human intestinal absorption\u003c/p\u003e\n\u003cp\u003eHRK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Harakiri/BCL-2 interacting protein\u003c/p\u003e\n\u003cp\u003eLR5\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lipinski\u0026rsquo;s rule of five\u003c/p\u003e\n\u003cp\u003eMCL-1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Myeloid cell leukemia-1\u003c/p\u003e\n\u003cp\u003eMD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Molecular dynamics\u003c/p\u003e\n\u003cp\u003eMDCK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Madin-Darby Canine Kidney Cells\u003c/p\u003e\n\u003cp\u003eMLL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mixed-lineage leukemia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMOMP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mitochondria outer membrane permeabilization\u003c/p\u003e\n\u003cp\u003eNCCN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Comprehensive Cancer Network\u003c/p\u003e\n\u003cp\u003eNSCLC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003ePapp\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Apparent permeability coefficient\u003c/p\u003e\n\u003cp\u003ePDB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Protein Data Bank\u003c/p\u003e\n\u003cp\u003ePDBQT\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Protein Data Bank, Partial Charge, \u0026amp; Atom Type\u003c/p\u003e\n\u003cp\u003ePgp\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;P-glycoprotein\u003c/p\u003e\n\u003cp\u003ePUMA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;p53 upregulated modulator of apoptosis\u003c/p\u003e\n\u003cp\u003eRCSB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Research Collaboratory for Structural Bioinformatics\u003c/p\u003e\n\u003cp\u003eRMSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Root mean square deviation\u003c/p\u003e\n\u003cp\u003eRMSF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Root mean square fluctuation\u003c/p\u003e\n\u003cp\u003eRoG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Radius of gyration\u003c/p\u003e\n\u003cp\u003eSASA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Solvent accessible surface area\u003c/p\u003e\n\u003cp\u003eSDF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Structure data file\u003c/p\u003e\n\u003cp\u003eTNFR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tumor necrosis factor receptor\u003c/p\u003e\n\u003cp\u003eTPSA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; - \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Topological polar surface area\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the study\u0026rsquo;s findings are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLOA\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026mdash;\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Data curation, Writing \u0026mdash; original draft, Writing \u0026mdash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFNA\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026mdash;\u003c/strong\u003e Methodology, Data curation, Writing \u0026mdash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOMO\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026mdash;\u0026nbsp;\u003c/strong\u003eMethodology, Data curation, Writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026mdash;\u0026nbsp;\u003c/strong\u003eMethodology, Data curation, Writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOAA\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026mdash;\u0026nbsp;\u003c/strong\u003eWriting\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSFH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026mdash;\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMAG\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026mdash;\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eA Study of Venetoclax and AMG 176 in Patients With Relapsed/Refractory Hematologic Malignancies - Full Text View - ClinicalTrials.gov\u003c/em\u003e. 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Deletion of MCL-1 causes lethal cardiac failure and mitochondrial dysfunction. \u003cem\u003eGenes \u0026amp; Development\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(12), 1351. https://doi.org/10.1101/GAD.215855.113\u003c/li\u003e\n\u003cli\u003eWen, Q., Zhan, Y., Zheng, H., Zang, H., Luo, J., Zhang, Y., Wang, W., Feng, J., Lu, J., Chen, L., \u0026amp; Fan, S. (2019). Elevated expression of mcl-1 inhibits apoptosis and predicts poor prognosis in patients with surgically resected non-small cell lung cancer. \u003cem\u003eDiagnostic Pathology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 1\u0026ndash;9. https://doi.org/10.1186/S13000-019-0884-3/TABLES/2\u003c/li\u003e\n\u003cli\u003eXiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., \u0026amp; Cao, D. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(W1), W5\u0026ndash;W14. https://doi.org/10.1093/NAR/GKAB255\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MCL-1, Bictegravir, BCL-2, Cabotegravir, Inhibitors, Cancer, Drug Repositioning","lastPublishedDoi":"10.21203/rs.3.rs-4412066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4412066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDysregulation of programmed cell death is a hallmark characteristic of cancer cells, making the apoptotic signaling pathway of important clinical relevance in cancer therapy. In mammalian cells, this critical cellular event is negatively regulated by antiapoptotic BCL-2 proteins. Notably, overexpression of Myeloid Cell Leukemia-1 (MCL-1) has emerged as a survival and drug resistance mechanism in several malignancies. Given its high oncogenic potential, MCL-1 represents an attractive therapeutic target for solid and hematological tumors. Oncological drug development is prohibitively expensive, time-consuming, and has a poor success rate due to toxic side effects. Thus, repurposing existing approved drugs with demonstrated safety profiles denotes a promising strategy for rapidly and economically discovering drugs in cancer medicine. Herein, we used a virtual computing technique to screen a customized library of thirty-one antiviral drugs for potential antagonistic activity against MCL-1. Our molecular docking experiment uncovered bictegravir and cabotegravir as promising inhibitors of MCL-1 in comparison to the reference clinical inhibitor (AMG176) based on superior binding affinity and strong interactions with the protein hotspots residues. Further, these integrase inhibitors exhibited appealing pharmacokinetic and toxicity profiles. Noteworthy, the thermodynamic parameters studied during the 100 ns molecular dynamics (MD) simulation and principal component analysis of the MD trajectory exemplify these drugs' structural stability and conformational flexibility in the protein active pocket. Our findings suggest that these integrase inhibitors could be repurposed for cancers overexpressing MCL-1. However, further studies involving experimental biological models are required to unravel their novel anticancer activity and ascertain their clinical efficacy in cancer treatment.\u003c/p\u003e","manuscriptTitle":"Disruption of Oncogenic MCL-1-BAX/BAK Interaction Using Integrase Inhibitors: Insights from a Molecular Docking and Dynamic Exploration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 06:34:25","doi":"10.21203/rs.3.rs-4412066/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63715d56-a474-4bfe-9f68-072bb46cba75","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-27T04:38:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-22 06:34:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4412066","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4412066","identity":"rs-4412066","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}