Machine Learning-Driven Discovery of a Lipid Nanoparticle for In-Vivo T-Cell Transfection in Non-Human Primates

Machine Learning-Driven Discovery of a Lipid Nanoparticle for In-Vivo T-Cell Transfection in Non-Human Primates | 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 Article Machine Learning-Driven Discovery of a Lipid Nanoparticle for In-Vivo T-Cell Transfection in Non-Human Primates Avi Schroeder, Nir Suissa, Eilam Yeini, Hiba Abu-Hariri, Igor Nudelman, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7848619/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 The limited availability of in-vivo transfection of T-cells with mRNA therapeutics remains a major bottleneck in the development of scalable and accessible gene and cell therapies​​. Lipid nanoparticles (LNPs) offer an in vivo, non-viral alternative to ex-vivo genetic engineering but have historically shown poor performance in T-cells​. Our machine learning approach enabled the rapid design of novel LNPs, seamlessly integrating in-silico prediction with wet-lab validation to accelerate the discovery and optimization process​. Here, we report the machine learning (ML)-guided discovery of FMB-3199, a passively targeted LNP capable of safe in vivo T-cell delivery without surface-conjugated ligands or antibodies, identified through iterative design-build-test-learn (DBTL) cycles that progressively refined and improved the model’s predictive quality. In NSG mice injected with human peripheral blood mononuclear cells (hPBMCs), FMB-3199 achieved ~ 60% transfection of human T-cells in vivo, further validating its translational potential. In addition, its analogs achieved up to 98% killing of NALM6 cells within 48 hours in vitro, underscoring their functional therapeutic efficacy. Finally, in non-human primates (NHPs), FMB-3199 enabled dose-dependent safe CD3⁺ T-cell transfection (~ 2.5–15%), with ~ 25% in CD4⁺ T-cells while minimizing liver uptake. Together, these findings establish a scalable and generalizable platform for in-vivo T-cell engineering, accelerating the development of next-generation mRNA-based cell therapies. Biological sciences/Immunology/Immunotherapy Biological sciences/Biotechnology/Gene delivery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lipid nanoparticles (LNPs) have revolutionized the field of nucleic acid delivery, emerging as the vehicle of choice for therapeutic payloads such as mRNA, siRNA, and gene-editing machinery​ 1 ​. Their prominence was catapulted into the global spotlight during the COVID-19 pandemic, where LNP-encapsulated mRNA vaccines enabled rapid, scalable, and safe immunization strategies​ 2 ​. LNPs offer a non-viral, highly tunable, and clinically validated alternative with proven performance across multiple therapeutic modalities​ 3 ​. A critical frontier in therapeutic delivery is the efficient transfection of immune cells, especially T lymphocytes​ 4 ​. These cells are central orchestrators of adaptive immunity and hold transformative promise in engineered cell therapies such as chimeric antigen receptor T-cell (CAR-T) therapy​ 5 ​. CAR-T therapies have demonstrated curative potential in hematologic malignancies, offering hope to patients with refractory or relapsed cancers​ 6 ​. However, the current ex-vivo CAR-T manufacturing paradigm is laborious, time-intensive, and costly, often requiring weeks of autologous cell processing under Good Manufacturing Practice (GMP) conditions, followed by lymphodepleting chemotherapy to facilitate engraftment​ 7 ​. In contrast, an in-vivo CAR-T approach, wherein T-cells are directly engineered within the patient using systemically delivered genetic payloads, offers a paradigm shift. It bypasses complex manufacturing, reduces cost and time to treatment, and potentially eliminates the need for preconditioning regimens​ 6 ​. Achieving this, however, requires the development of delivery systems that can transfect non-activated T-cells in circulation with high efficiency, specificity, and safety-a feat that has eluded the field due to the intrinsic resistance of resting T-cells to nucleic acid uptake​ 4 ​. The lipid chemical space is vast and highly modular, comprising a near-infinite array of possible combinations of headgroups, tails, linkers, and functional motifs​ 8 ​. Traditionally, LNP development has relied heavily on trial-and-error, empirical formulation, and expert-driven intuition-a laborious and inefficient process that struggles to explore the full combinatorial landscape of possible candidates​ 9 ​. In recent years, ML has emerged as a powerful tool for hypothesis-free design across multiple domains, from protein folding and molecular dynamics to clinical diagnostics and materials science​ 10 ​. In the context of nanoparticle development, ML offers the potential to quickly navigate the chemical space, identify high-performing formulations, and optimize for complex, multi-dimensional criteria such as potency, selectivity, and safety​ 11 ​. In this study, we demonstrate the power of a machine-learning-guided lipid nanoparticle design platform to achieve efficient in-vivo transfection of T-cells in-vitro, mice and non-human primates within a six-month development timeline. Our approach leverages a data-rich DBTL framework that integrates literature-derived and proprietary datasets, high-throughput experimental feedback, and predictive modeling. We identify FMB-3199 as a safe, stable, and effective LNP capable of delivering mRNA to CD3⁺, CD4⁺, and CD8⁺ T-cells with minimal liver transfection and favorable toxicity profile. This work establishes a foundation for rapid, in-vivo engineering of immune cells and opens a pathway toward scalable, patient-friendly T-cell therapies for oncology and autoimmune indications. Results Machine Learning Model Development and Foundational Dataset Construction To establish a strong model foundation, we first curated a comprehensive public-domain dataset of lipid nanoparticle (LNP) formulations for T-cell transfection from the literature and patents, comprising over 1,500 datapoints describing transfection efficiency, viability, and physico-chemical properties. With each cycle of DBTL (Fig. 1 a), the model was iteratively refined using proprietary wet-lab data: ~1,500 transfection datapoints, ~ 1,500 viability measurements, ~ 3,500 physico-chemical (PC) descriptors, and ~ 4,000 safety datapoints, including THP-1 activation, Alanine Aminotransferase (ALT), Aspartate Aminotransferase (AST) and cytokine release (Fig. 1 b). These additions increased both model accuracy and formulation diversity, as evidenced by the progressive data accrual and steady increase in top predictive performance across cycles. Using this dataset, we trained two initial machine learning (ML) models to predict transfection efficiency and cell viability of LNPs in non-activated human T-cells. The transfection model achieved a mean absolute error (MAE) of 3.58 and Pearson correlation of 0.79, as shown in Fig. 1 c. The viability model achieved MAE of 13.71 and Pearson correlation of 0.72 (Fig. 1 d). Safety is a crucial aspect in LNP formulations, and to incorporate it into our screening process we developed predictive models for key safety-related assays, including ALT, AST, THP-1 activation, and cell viability (Fig. 1 e). These models improved steadily over time through weeks of iterative DBTL cycles. Each week, newly generated wet-lab data from novel formulations were added to the database, and the model produced predictions for all four assays. The predictions were then validated experimentally, and correlation scores between predicted and observed values were calculated to quantify performance. These results were fed back into the database to refine the models in the subsequent iteration. With weekly data integration and iterative retraining, all models showed progressive and consistent gains in predictive accuracy, highlighting the importance of feedback-driven refinement for reliable LNP safety screening. In Vitro Proof-of-Concept and LNP Formulation Screening To ensure that the testing system operates effectively, we assembled an LNP library that not only included our proprietary novel formulations but also rationally selected immune-active molecules (e.g., MB-5132), known formulations used as controls (MC3, SM-102 and LP-01) and literature-reported lipids previously explored for T-cell transfection (Fig. 2 a). The general assembly and naming scheme are shown in Fig. 2 b-c. Candidate head-linker-tail combinations were scored in silico and ranked by our model.Variant libraries were constructed for each lipid class and combinatorially assembled (Fig. 2 d, Table 2 ). Formulations were then generated by independently varying component ratios, ranked by our model, and the top set was selected. Compositions of the top set and controls are summarized in the formulation ratios heat map (Fig. 2 e). Top-ranked candidates and controls were synthesized and tested in primary human T-cells (activated and non-activated) in collaboration with Sheba Medical Center. Flow cytometry analysis showed that several candidates enabled significant transfection in both activated and non-activated T-cells, with model-nominated formulations performing notably better than the control - polyplus JetMessenger 5k (Fig. 3 a). Model-Driven Iterations and hPBMC Optimization Following the initial screen, DBTL cycles transitioned to hPBMCs, enabling more scalable and diverse profiling. After each round, transfection and viability results were fed back into the model to retrain and reprioritize future candidates. In these hPBMC studies, in-silico formulation design differed substantially depending on activation state; Formulations that ranked highly in activated T-cells generally showed limited response in non-activated cells, and vice versa, yielding largely non-overlapping performance profiles with only a small, shared subset (Fig. 3 b). Because non-activated cells are harder to transfect, evaluation criteria were adjusted for each state and included as a feature across the models (Fig. 3 c). This data indicates that success factors differ between activated and non-activated T-cells which perhaps argues for state-specific optimization. Lead-candidate LNPs support potent eGFP and CAR mRNA delivery across T-cell subsets To identify lead formulations capable of efficient T-cell transfection, we conducted a series of DBTL optimization cycles in hPBMCs. In non-activated T-cells-considered one of the most challenging targets for mRNA delivery-the top four machine learning-nominated lipid nanoparticles (LNPs) achieved between 30 to 40% eGFP expression (Fig. 4 d). This performance represented a > 10-fold improvement relative to the benchmark ionizable lipid LP-01 and the commercial transfection reagent Polyplus JetMessenger 5k, both of which failed to exceed 5% efficiency in this setting. Activated T-cells exhibited higher permissiveness to LNP-mediated delivery, with candidates achieving 70–90% eGFP expression (Fig. 4 e), except FMB-3999 which performed best in non-activated T-cells, establishing a clear potency gradient dependent on activation status. To evaluate the translational potential of these LNPs for therapeutic payloads, CD19 chimeric antigen receptor (CAR) mRNA (GeneScript) was formulated into high-ranked candidates in parallel with eGFP formulations, and transfection efficiency was assessed in CD3⁺ T-cells. These experiments demonstrated successful delivery of functional CAR mRNA, with expression rates reaching ~ 25% in CD3⁺ cells (Fig. 4 a). Further dissection of transfection across T-cell subsets revealed peak CAR expression levels of ~ 35% in CD4⁺ T-cells and substantial transfection in CD8⁺ populations as well (Fig. 4 b). Across all conditions, transfection was achieved without compromising cell viability, and results were consistent across biological replicates (n = 3 donors). These data confirm the ability of ML-optimized LNPs to robustly deliver both reporter and functional therapeutic mRNA into key immune cell subsets, highlighting their potential for in-vivo engineering of T-cells in cancer and autoimmune settings. To validate the activity of the LNP-delivered CAR mRNA, we performed cytotoxicity assays against NALM6 leukemia B cells. At an effector-to-target (E:T) ratio of 10:1, two lead formulations (FMB-3199 and FMB-3258) showed robust activity, reaching 95% and 98% target-cell killing by 72h respectively, compared with 65% for the SM-102 control (Fig. 4 c). At an E:T ratio of 5:1, the same formulations achieved 74% and 88% killing by 72h respectively, again outperforming the control (48%; Fig. 4 d). In a subsequent optimization step, we evaluated additional formulations which are FMB-3199 analogs, modifying the helper lipid component while keeping the rest of the formulation identical. Thereby identified two analogs that surpassed FMB-3258, yielding up to 97% in target-cell elimination at 48h, E:T ratio of 5:1 (Fig. 4 e). In Vivo Validation in Humanized Mice and Dose-Finding in Rats To validate performance in vivo, female NSG mice were injected with 20M hPBMCs followed by intravenous LNP administration (0.65 mg/kg). After five DBTL cycles, four lead candidates achieved between 25–55% transfection in circulating human T-cells-substantially higher than the LP-01 reference (Fig. 5 a). FMB-3199, which in vitro showed expression largely restricted to activated T-cells, displayed a broader in vivo profile of ~ 20% eGFP positivity in non-activated s and ~ 30% in activated s (Fig. 5 b). The DRF study in rats (females, n = 3 rats per group) defined the safety profile of our three lead LNP candidates. Dose escalation to 0.5, 0.9, and 1.2 mg/kg was well tolerated: liver enzymes, cytokines, and coagulation parameters remained stable over 24 hours post-dose, and liver histology showed no treatment-related abnormalities, supporting hepatic tolerability (Fig. 5 a-h). Formulation Stability and Freeze-Thaw Performance Six lead formulations were tested for size, polydispersity index (PDI), and encapsulation efficiency (EE) before and after dialysis and freeze-thaw process. FMB-3199 showed great stability, retaining ~ 97 nm size, PDI 84%, and ~ 80% transfection efficiency activity post-thaw. Table 1 physico-chemical stability of lead formulations. Particle size, polydispersity index (PDI) and encapsulation efficiency (EE %) measured immediately after formulation, post-dialysis and after one freeze-thaw cycle. FMB-3199 maintains ~ 96 nm diameter, PDI ≈ 0.06 and EE > 84 %, indicating preserved physicochemical characteristics. Direct Dialysis Thaw FMB Size (nm) SD PDI SD EE (%) SD Size (nm) SD PDI SD EE (%) SD Size (nm) SD PDI SD EE (%) SD FMB-3999 94.71 0.53 0.01 0.02 84 4.61 108.07 0.38 0.08 0.03 84.2 4.61 110.17 0.38 0.2 0.05 80.95 2.3 FMB-3258 95.17 0.45 0.05 0.04 91.2 4.61 109.03 0.23 0.06 0.02 89.6 4.61 108.83 0.61 0.15 0.05 87.21 1.69 FMB-3875 100.85 0.53 0.12 0.02 97.1 3.07 123.9 0.23 0.08 0.64 93.5 3.07 123.37 0.3 0.13 0.05 96.32 1.99 FMB-3876 77.71 0.45 0.04 0.04 90.7 5.38 85.23 0.45 0.16 0.02 87 5.38 89.79 0.45 0.27 0.05 82.16 3.42 FMB-3879 79.01 0.23 0.07 0.02 93.1 2.3 80.81 0.45 0.16 0.05 89 2.30 97.97 0.45 0.22 0.02 86.59 1.71 FMB-3199 95.96 0.61 0.07 0.02 84.3 461 96.41 0.53 0.05 0.02 86.5 4.61 98.42 0.38 0.06 0.05 90.29 2.68 Non-Human Primate Studies Validated FMB-3199 as Lead Candidate FMB-3199 was evaluated in NHPs across a dose range of 0.5, 0.9, and 1.2 mg/kg to assess its transfection efficiency, biodistribution, and preliminary safety. A robust and reproducible dose-response was observed in circulating CD3⁺ T-cells, with transfection efficiencies of ~ 2.5% at the low dose, ~ 8% at the intermediate dose, and ~ 15% at the highest tested dose (Fig. 6 a). This linear trend underscores the formulation’s pharmacological scalability and supports dose titration for future clinical settings. This trend extended to T-cell subsets where transfection in CD4⁺ T-cells was particularly pronounced, reaching up to ~ 25% at the 1.2 mg/kg dose, while CD8⁺ T-cells exhibited ~ 8% transfection, suggesting differential uptake and/or expression dynamics across T-cell compartments (Fig. 6 b). Importantly, off-target hepatic expression was minimal across all dose levels, with transgene signal restricted to ~ 2% of hepatocytes (Fig. 6 c). This low hepatic tropism contrasts with conventional LNPs such as SM-102 and MC3, which show strong liver enrichment 25 . Along with reduced hepatic uptake, B-cells delivery reached ~ 7%, reflecting the success of our ML-guided design strategy aimed at immune-cell selectivity and liver avoidance. Toxicity panels over 24 hours after high dose with FMB-3199showed mildly elevated liver enzymes, transient mild elevation in 2/5 cytokines, and no abnormalities in coagulation or CBC, consistent with a benign and well-tolerated safety profile across parameters (Fig. 6 d-j). Liver histopathology confirmed no adverse findings. Hematoxylin and eosin (H&E) staining of liver sections from FMB-3199-treated non-human primates revealed intact hepatic lobular architecture, with no signs of necrosis, inflammatory infiltrates, fibrosis, or bile duct injury. Across all tested doses, liver morphology remained indistinguishable from vehicle-treated controls, supporting the overall safety and hepatic tolerability of the formulation (Fig. 9k,l). These findings establish FMB-3199 as a highly safe, and stable LNP candidate for future applications in T-cell engineering and mRNA-based immunotherapies. Discussion CAR T-cell therapy has produced remarkable outcomes across several indications. Nonetheless, most approaches still require ex vivo modification which carries safety concerns and demands long, costly manufacturing​ 7 ​. Therefore, transfecting T- cells in vivo holds great potential for an alternative therapeutic approach​ 3 ​. However, identifying safe, effective in vivo delivery systems and conditions remains challenging. This complexity makes traditional trial-and-error methods too slow and costly​ 12 ​. In this study we demonstrate the power and potential of AI-guided workflow, to accelerate discovery. Initially, we compiled literature readouts into a dataset suitable for modeling; we then applied an ensemble of predictors in a DBTL workflow to nominate LNPs and evaluated them in vitro in-house. The results were integrated into the model's training set to improve its predictive accuracy. With this strategy as a process, we nominated a selection of novel candidates that exceeded the benchmark both in vitro and in vivo. A central hurdle for in-vivo T-cell engineering is that efficient delivery typically improves with cell activation, but couples to rapid expansion​ 14 ​. Transfecting non-activated T-cells, which exhibit superior differentiation characteristics and reduced exhaustion, poses a greater delivery challenge​ 14 ​. Therefore, both cell states remain viable targets, with distinct trade-offs. Hence, from the very first screen we evaluated the formulation’s efficiency in both activated and non-activated hPBMC T-cells, and the T-cell state was introduced to the model as a feature. By leveraging rational design and model-based screening, we were able to identify LNPs capable of preferentially transfecting either activated or non-activated cells, as well as formulations with balanced activity across both states. This flexibility constitutes a strategic advantage, as it enables the tailoring of LNP formulations to distinct therapeutic indications-whether favoring activated T-cells for rapid immune responses, non-activated T-cells for long-lived persistence, or a combination of both for broader efficacy. Moreover, our platform highlights LNPs optimized for ex-vivo transfection of non-activated T-cells, enabling CAR-T manufacturing with minimal activation while preserving viability. This reflects the incorporation of domain knowledge into the AI pipeline via expert-guided feature selection and state-aware objectives, yielding fit for purpose candidates. Translating our findings in vivo, we conducted five DBTL cycles in NSG mice using both activated and non-activated hPBMC T-cells. FMB-3199, a newly proposed candidate, surpassed the in-vitro transfection threshold and demonstrated superior transfection rates compared to other LNP candidates, achieving approximately 20% in non-activated cells and 30% in activated cells. Moreover, in NHPs, FMB-3199 maintained cross-species performance, with transfection increasing with dose in both circulating CD3⁺ T-cells and their subsets. Yet another concern is that CAR-T gene-therapy vectors must specifically target T-cells while minimizing off-target uptake and transfection, as these can pose safety risks​ 15 ​. This is a primary concern for LNPs, which exhibit strong hepatic tropism, which can result in dose-limiting hepatotoxicity if liver exposure is not adequately controlled​ 16 ​. The safety parameters evaluated in NHPs are not only critical indicators of tolerability in preclinical studies but also directly relevant to the clinical advancement of in vivo CAR-T platforms​ 20 ​. Liver enzymes such as ALT and AST serve as sensitive markers of hepatocellular stress​ 18 ​; their mild transient changes in FMB-3199-treated animals demonstrates that the formulation avoids the hepatotoxicity commonly observed with conventional LNPs​ 17 ​. This is particularly important for clinical translation, where repeated dosing may be required​ 19 , and hepatic safety often defines the upper bound of the therapeutic window​ 20 ​. Cytokine monitoring provides insight into systemic immune activation, as excessive release of pro-inflammatory mediators can lead to cytokine release syndrome (CRS)-a major clinical concern in CAR-T therapy​ 21 ​. The observation that key cytokines (IP10, IL6, IFN-g, TNF-a and IL1b) remained at baseline or showed transient mild elevation, supports the notion that FMB-3199 engages T-cells without triggering pathological systemic inflammation. Finally, coagulation profiling is essential, as several nucleic acid-based therapies have been associated with thrombogenicity​ 22 ​; maintaining physiological coagulation parameters in NHPs suggests a low risk of infusion-related coagulopathies in humans​ 23 . Taken together, these safety readouts provide mechanistic reassurance that FMB-3199 combines efficient T-cell targeting with a benign systemic safety profile, thereby strengthening confidence in its suitability for in-vivo CAR-T applications and its potential to advance safely into the clinic. For clinical translatability, it is essential to evaluate LNPs with the actual therapeutic payload​ 24 ​. Accordingly, we confirmed efficient in-vitro delivery of CD19 CAR mRNA to CD3⁺ T-cells and their CD4⁺/CD8⁺ subsets using our lead LNP which retained its delivery and functional properties upon payload exchange, as evidenced by robust CD19-directed killing. Given the favorable safety profile of FMB-3199, we then examined closely related structural analogs designed to enhance performance while preserving safety. Guided by our ML models, these analogs demonstrated improved activity, achieving ~ 98% killing in co-culture assays. The favorable balance between efficacy and safety observed in NHPs positions FMB-3199 as a promising candidate for further clinical development. The linear dose-response in T-cell transfection, combined with minimal hepatic off-target expression and an excellent tolerability profile, suggests that higher doses could be explored to further increase transfection efficiency without compromising safety. In particular, the absence of hepatotoxicity, stable liver histopathology, and only transient cytokine changes across the tested range indicate a wide therapeutic window for this formulation. Thus, beyond demonstrating proof-of-concept, these findings support the potential for dose escalation strategies aimed at achieving enhanced T-cell modification rates, while maintaining the benign safety profile that distinguishes FMB-3199 from conventional LNP platforms. In summary, we demonstrate how an AI-guided, closed-loop workflow (we need to show an example of how the molecules and formulations change with between the cycles) can automate and elevate experimental design, data integration, and analysis. By fusing existing literature, in-house datasets, and predictive models within rapid design-build-test-learn cycles, we efficiently explored novel lipids and identified a lead LNP with selective T-cell transfection and a favorable safety window in vivo, supporting its therapeutic potential and paving the way for further preclinical development. Methods & Materials Lipid synthesis and formulation Lipid synthesis: Lipids were synthesized according to the general scheme (Figure 2b). Ionizable lipids were synthesized through epoxide ring opening or Michael addition with a polyamine core under heat in ethanol followed by silica purification. Formulations were assembled from Lipids and aqueous phase. The aqueous phase was prepared by mixing eGFP mRNA (GenScript, custom sequence) with citrate buffer (200 mM, pH 4.9-5.1) which was prepared by dissolving citric acid (Bio-Lab, #000302059100) and trisodium citrate (Fisher Chemicals, #10396430), followed by sterile filtration through a 0.22 μm membrane, and molecular-grade water (Sartorius, #01-869-1A) according to N:P 4 calculation. Lipids were dissolved in ethanol, heated for 10 min, and combined into formulation-specific mixtures, which were reheated at 55 °C for 10 min. Lipid and aqueous phases were loaded into syringes and combined using a microfluidics chip (Cytiva, #NIN0062) operated with the NanoAssemblr program at a total flow rate of 12 mL/min. Formulations were buffer-exchanged into a cryoprotective solution (molecular-grade water, 1% Tris-HCl [0.5 M, Thermo, #J67501.AK], 12.5% sucrose [JT Baker, #4072]) by overnight dialysis at 4°C using a 20 kDa Float-A-Lyzer (Merck, Z726931-12EA). Samples were cryopreserved in 1.8-mL cryovials at −80 °C until use. Physicochemical characterization of LNPs was performed both post-dialysis and after thawing to confirm LNPs integrity. Table 2 | The ionizable-lipid library was used for model-based ranking. The model-selected top set was subsequently evaluated in vitro. Component Variant Component Variant IonIzable 1 DLin-MC3-DMA PEG 1 DMG-PEG2000 IonIzable 2 c12-200 PEG 2 c14-peg2000 IonIzable 3 R1bR2bR3d PEG 3 DSG-PEG2000 IonIzable 4 R1dR2aR3a PEG 4 DSPE-PEG2000 IonIzable 5 cKK-E12 PEG 5 ALC-0159 IonIzable 6 ALC-0315 PEG 6 Brij S20 IonIzable 7 R1bR2bR3c PEG 7 MB-5035 IonIzable 8 SM-102 Additional lipid 1 MB-5132 IonIzable 9 R1cR2bR3c Additional lipid 2 Vitamin-D2 IonIzable 10 R1dR2bR3d Additional lipid 3 Vitamin-D3 IonIzable 11 R1aR2aR3a Additional lipid 4 MB-5057 Helper 1 DOPE Additional lipid 5 MB-5112 Helper 2 DSPC Additional lipid 6 DOTAP Helper 3 SOPC Additional lipid 7 MB-5024 Helper 4 Triolein Helper 5 DOPG Helper 6 Diolein Helper 7 MB-5118 Helper 8 MB-5132 Physicochemical characterization Formulations were diluted to 20 µg/mL in PBS (Sartorius, #02-023-1A). Particle size and polydispersity index (PDI) were determined either by diluting 50 µL of formulation in 450 µL PBS and measuring by dynamic light scattering (Zetasizer Nano ZSP, Malvern Instruments), or by diluting 5 µL in 800 µL PBS and dispensing 100 µL per well in triplicate into clear 96-well plates for analysis on the Dynapro. Encapsulation efficiency (EE%) was assessed with the Quant-iT RiboGreen assay (Thermo Fisher Scientific, R11490). Briefly, 5 µL of formulation was dispensed in triplicate into black 96-well plates, followed by 45 µL TE buffer and 50 µL RiboGreen (1:100). After 5 min at 37°C, fluorescence corresponding to unencapsulated RNA was measured (Tecan plate reader). Samples were then lysed with 50 µL 2% Triton (Sigma, #102671448) and re-read to determine total RNA. EE% was calculated as Zeta potential was measured after diluting 100 µL of formulation in 1.9 mL 10% sucrose (Sigma, #0389-1KG, pH 7.20-7.45) using a DTS1070 cuvette on the Zetasizer Nano ZSP. Apparent pKa was determined using the TNS assay (Abcam, #ab275049): formulations were incubated in 12 buffers spanning a pH gradient, followed by addition of TNS dye. Fluorescence was recorded (Tecan plate reader with automated liquid handler), normalized as (fluorescence − minimum)/(maximum − minimum), and fitted to a sigmoidal titration curve. The pKa was defined as the pH at half-maximal fluorescence. Cell culture and in vitro assays A549 cells were maintained in Complete medium). Cells (2 × 10⁴ per well) were seeded in white 96-well plates and treated in triplicate with test formulations (0.5, 1, or 3 µg/mL). After 24 h, viability was assessed using the CellTiter-Fluor™ assay (Promega, #G6082). THP-1 NF-κB reporter cells were cultured with complete medium (RPMI 1640 medium (Sartorius, #01-100-1A) supplemented with 10% FBS (Gibco, #A5256801), 1% penicillin-streptomycin (Biowest, #L0022-100), and 1% L-glutamine (Sartorius, #03-020-1A)s, supplemented with 25 mM HEPES (Sartorius, #03-025-1B) and 100 µg/mL zeocin (InvivoGen, #ant-zn-1), and maintained up to passage 20. Cells (2.5 × 10⁴ per well) were seeded into 96-well plates and treated with formulations (0.5, 1, or 3 µg/mL). LPS (1 µg/mL; Merck, #L2387-10MG) served as a positive control. After 48 h, alkaline phosphatase activity was measured by transferring 20 µL supernatant into 180 µL Quanti-Blue™ reagent (InvivoGen, #rep-qbs2) and reading OD at 620 nm. Reporter activity was normalized to viability (CellTiter-Fluor™). hPBMCs Human peripheral blood mononuclear cells (PBMCs) were obtained from whole blood (Magen David Adom, Israel) by density gradient separation with Lymphoprep™ (Stemcell Technologies, #18061) following dilution (1:1 with PBS) and centrifugation at 800 × g for 20 min (brakes off). The PBMC layer was collected, washed, and cryopreserved in NutriFreez® (Sartorius, #05-713-1A). PBMCs either isolated in-house or obtained from ‘CellGeneration’, were thawed and seeded into culture flasks at a density of 1 × 10⁶ cells/mL in complete medium (as described above). For activation, PBMCs were stimulated with TransAct (Miltenyi Biotec, 130-111-160) at a 1:100 dilution and recombinant human IL-2 (PeproTech, #200-02) at a final concentration of 50 ng/mL. Activated PBMCs were cultured for 72 hours prior to plating for treatment, whereas non-activated PBMCs were incubated overnight. Cells were seeded at a density of 1 × 10⁶ cells/mL and treated for 20 h with either GFP mRNA (3 µg/mL) or CD19 CAR-T mRNA (6 µg/mL) formulations. Following incubation, cells were washed and analyzed by flow cytometry (FC) or subjected to cytotoxicity assays. JetMessenger transfection reagent (Polyplus, #101000056) was used as a positive control for flow cytometry, while untreated cells served as negative controls for both FC and cytotoxicity assays. Culture supernatants were collected and stored at −20 °C for subsequent ELISA analysis. ELISA Cytokine concentrations in cell culture supernatants were quantified using ELISA kits (R&D Systems; human IL-6, #D6050; human TNF-α, #DTA00D; human IFN-γ, #DIF50C) according to the manufacturer’s instructions Flow Cytometry Cells were washed with PBS and incubated with a viability dye for 30 minutes at room temperature. Following additional PBS wash, cells were treated with Fc block for 15 minutes and subsequently stained with fluorophore-conjugated antibodies specific for the desired markers. Cells were washed again with FACS buffer - PBS 0.5% BSA (SIGMA, #03117057001) 0.5mM EDTA (JT Baker, #8993-01) Samples were either acquired immediately on a Northern Lights flow cytometer (Cytek) or fixed prior to acquisition. FC data were analyzed using FlowJo software (BD Biosciences). Category Item Vendor Catalog Number Blocking reagent Human Fc block (TruStain FcX) BioLegend 422302 Antibody Anti-mouse CD4⁺5 Miltenyi 130-110-798 Antibody Anti-mouse Ter119 Miltenyi 130-112-910 Antibody Anti-human CD3⁺ Miltenyi 130-129-580 Antibody Anti-human CD4⁺ BioLegend 300539 Antibody Anti-human CD8⁺ Miltenyi 130-110-680 Antibody Anti-human CD5 BioLegend 300644 Antibody Anti-human CD69 Miltenyi 130-112-805 Antibody Anti-human CD11b Miltenyi 130-131-840 Antibody Anti-human CD56 BioLegend 362578 Antibody Anti-human CD14 BioLegend 301828 Antibody Anti-human CD19 BioLegend 302230 Antibody Anti-FMC63 FITC AcroBiosystems FM3-FY45-25tests Antibody Anti-FMC63 Miltenyi 130-127-344 Killing assay For cytotoxicity assays, NALM6-luciferase cells were maintained in complete medium (described above). Cells (1 × 10⁴/well) were seeded into white 96-well plates and co-cultured with treated PBMCs at a 5:1 effector-to-target ratio for 48 h. Luminescence was quantified using ONE-Glo (Promega, #E6110) on a Tecan plate reader. Killing was normalized to untreated PBMC controls. In vivo studies NSG mice (8-10 weeks; Jackson Laboratory) were obtained via Vivox. Mice were injected intravenously with 2 × 10⁷ human PBMCs, and 24 h later received formulations at 0.65 mg/kg. Blood was collected 24 h post-treatment; mice were then euthanized, and organs harvested for FC. All procedures were approved by the institutional animal ethics committee. For dose-range finding (DRF), Sprague-Dawley rats (Jackson Laboratory) received 0.5, 0.9, or 1.2 mg/kg via Vivox. Blood was collected at 1, 6, and 24 h for serum chemistry, coagulation, cytokines, and CBC. Rats were euthanized, and livers processed for histology. For PBMC isolation from mouse blood, samples were diluted to 2 mL with PBS. RBC lysis was performed with 5 mL of 1× RBC lysis buffer (BD, #555899) for 10 min at RT, quenched with RPMI + 10% FBS, and centrifuged at 300 × g for 5 min. Lysis was repeated up to three times if erythrocytes remained. Cells were resuspended in PBS and stained for FC. NHP study design and animals Seven animals were enrolled to evaluate two distinct LNP formulations encapsulating an eGFP mRNA payload. Animals were randomly assigned to receive escalating intravenous (IV) doses of either formulation across three consecutive treatment days. All procedures were conducted in compliance with relevant ethical regulations for animal research. Formulations and dosing regimen Animals received formulation FMB-3199. The dosing schedule for FMB-3199 was as follows: 0.9 mg/kg on Day 1, 0.5 mg/kg on Day 2, and 1.2 mg/kg on Day 3. For the final administration, animals were pretreated 1h prior to infusion with dexamethasone (1 mg/kg, IV), famotidine (0.5 mg/kg, IV), and diphenhydramine (5 mg/kg, IV). All injections were administered as a continuous IV infusion over 1h. Blood collection and cellular assays Peripheral blood was collected at baseline (pre-dose),1h, 6h, and 24h post-infusion. T-cells and B-cells were isolated and assayed for eGFP expression to assess transfection efficiency. In addition, clinical chemistry, complete blood counts (CBC), coagulation and cytokines panels were performed at all collection timepoints. Tissue collection and histology At 24h following the final administration, liver biopsies were collected. Tissues were fixed in 10% neutral buffered formalin, embedded in paraffin, sectioned, and stained with hematoxylin and eosin for histopathological evaluation. References Hou, X., Zaks, T., Langer, R. & Dong, Y. Lipid nanoparticles for mRNA delivery. Nature Reviews Materials vol. 6 Preprint at https://doi.org/10.1038/s41578-021-00358-0 (2021). Zhang, L. et al. Effect of mRNA-LNP components of two globally-marketed COVID-19 vaccines on efficacy and stability. NPJ Vaccines 8 , (2023). Guan, S. & Rosenecker, J. Nanotechnologies in delivery of mRNA therapeutics using nonviral vector-based delivery systems. Gene Therapy vol. 24 Preprint at https://doi.org/10.1038/gt.2017.5 (2017). Tombácz, I. et al. Highly efficient CD4+ T cell targeting and genetic recombination using engineered CD4+ cell-homing mRNA-LNPs. Molecular Therapy 29 , (2021). Benmebarek, M. R. et al. Killing mechanisms of chimeric antigen receptor (CAR) T cells. International Journal of Molecular Sciences vol. 20 Preprint at https://doi.org/10.3390/ijms20061283 (2019). Álvarez-Benedicto, E. et al. Spleen SORT LNP Generated in situ CAR T Cells Extend Survival in a Mouse Model of Lymphoreplete B Cell Lymphoma. Angewandte Chemie - International Edition 62 , (2023). Billingsley, M. M. et al. Ionizable Lipid Nanoparticle-Mediated mRNA Delivery for Human CAR T Cell Engineering. Nano Lett 20 , (2020). Tenchov, R., Bird, R., Curtze, A. E. & Zhou, Q. Lipid Nanoparticles from Liposomes to mRNA Vaccine Delivery, a Landscape of Research Diversity and Advancement. ACS Nano vol. 15 Preprint at https://doi.org/10.1021/acsnano.1c04996 (2021). Wang, W. et al. Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm. Acta Pharm Sin B 12 , (2022). Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discovery Today vol. 23 Preprint at https://doi.org/10.1016/j.drudis.2018.01.039 (2018). Maharjan, R. et al. Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach. Int J Pharm 640 , (2023). Zugasti, I. et al. CAR-T cell therapy for cancer: current challenges and future directions. Signal Transduction and Targeted Therapy vol. 10 Preprint at https://doi.org/10.1038/s41392-025-02269-w (2025). Radhakrishnan, H., Newmyer, S. L., Ssemadaali, M. A., Javitz, H. S. & Bhatnagar, P. Primary T-cell-based delivery platform for in vivo synthesis of engineered proteins. Bioeng Transl Med 9 , (2024). Ghassemi, S. et al. Rapid manufacturing of non-activated potent CAR T cells. Nat Biomed Eng 6 , (2022). Billingsley, M. M. et al. In Vivo mRNA CAR T Cell Engineering via Targeted Ionizable Lipid Nanoparticles with Extrahepatic Tropism. Small 20 , (2024). Khawar, M. B., Afzal, A., Si, Y. & Sun, H. Steering the course of CAR T cell therapy with lipid nanoparticles. Journal of Nanobiotechnology vol. 22 Preprint at https://doi.org/10.1186/s12951-024-02630-1 (2024). Cruz, M. Recent advances in clinical and therapeutic approaches to FAP. Brain Pathology 24 , (2014). Thakur, S., Kumar, V., Das, R., Sharma, V. & Mehta, D. K. Biomarkers of Hepatic Toxicity: An Overview. Current Therapeutic Research - Clinical and Experimental vol. 100 Preprint at https://doi.org/10.1016/j.curtheres.2024.100737 (2024). Hunter, T. L. et al. In Vivo CAR T Cell Generation to Treat Cancer and Autoimmune Disease . https://www.science.org. Brudno, J. N. & Kochenderfer, J. N. Recent advances in CAR T-cell toxicity: Mechanisms, manifestations and management. Blood Reviews vol. 34 Preprint at https://doi.org/10.1016/j.blre.2018.11.002 (2019). Lin, M. Y. et al. Self-regulating CAR-T cells modulate cytokine release syndrome in adoptive T-cell therapy. Journal of Experimental Medicine 221 , (2024). Li, J. et al. Systemic toxicity of CAR-T therapy and potential monitoring indicators for toxicity prevention. Frontiers in Immunology vol. 15 Preprint at https://doi.org/10.3389/fimmu.2024.1422591 (2024). Yagyu, S. et al. A lymphodepleted non-human primate model for the assessment of acute on-target and off-tumor toxicity of human chimeric antigen receptor-T cells. Clin Transl Immunology 10 , (2021). Li, S. et al. Payload distribution and capacity of mRNA lipid nanoparticles. Nat Commun 13 , (2022). Liu, Y. et al. Development of mRNA Lipid Nanoparticles: Targeting and Therapeutic Aspects. International Journal of Molecular Sciences vol. 25 Preprint at https://doi.org/10.3390/ijms251810166 (2024). Additional Declarations Yes there is potential Competing Interest. N.S., E.Y., I.N., G.R., L.G., M.H., N.R., D.M., N.S., and I.L. are employees of Mana.bio. A.S. is affiliated with both Mana.bio and the Technion. All other authors are affiliated with Sheba Medical Center and have no competing financial interests. 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. 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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-7848619","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":543954200,"identity":"da814f6d-8d76-4aa4-b033-c7fcb8b65f4c","order_by":0,"name":"Avi 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Lupovitz","email":"","orcid":"","institution":"Mana.bio","correspondingAuthor":false,"prefix":"","firstName":"Inbal","middleName":"","lastName":"Lupovitz","suffix":""},{"id":543954212,"identity":"5beed57f-1b23-42e3-b339-b35ce5ad7a11","order_by":12,"name":"Anat Shemer","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Anat","middleName":"","lastName":"Shemer","suffix":""},{"id":543954213,"identity":"1303186a-4708-4d1b-a208-11f7dcc9bbf2","order_by":13,"name":"Gilad Gibor","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Gilad","middleName":"","lastName":"Gibor","suffix":""},{"id":543954214,"identity":"1b7799bb-ef19-40dd-925d-aa1f754a3100","order_by":14,"name":"Ronnie Shapira-Frommer","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ronnie","middleName":"","lastName":"Shapira-Frommer","suffix":""},{"id":543954215,"identity":"81e561ec-8ab0-4d6d-9f4f-de9ec7ec5de6","order_by":15,"name":"Yochai Wolf","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yochai","middleName":"","lastName":"Wolf","suffix":""},{"id":543954216,"identity":"a2fc52f0-e696-4bf0-a56c-155ab8f3dc6a","order_by":16,"name":"Gal Cafri","email":"","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Gal","middleName":"","lastName":"Cafri","suffix":""}],"badges":[],"createdAt":"2025-10-13 11:41:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7848619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7848619/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97404011,"identity":"6953769c-c3d1-4ebf-8627-6a7f57a99d0b","added_by":"auto","created_at":"2025-12-04 03:09:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMachine-learning model construction and evolution. \u003c/strong\u003ea, Line chart showing progressive improvement in model accuracy over iterative design-build-test-learn rounds. b, Cumulative number of proprietary datapoints generated across successive DBTL cycles (≈1,500 transfection, 1,500 viability, 3,500 physicochemical, 4,000 safety), illustrating rapid expansion of the training corpus. c, Scatter plot of predicted versus observed transfection efficiency for the baseline LNP model (1,666 public data points); performance metrics: MAE = 3.58 and Pearson r = 0.79. d, Equivalent analysis for the cell-viability model (MAE = 13.71; r = 0.72). e, Safety predictions models score improvement over time - ALT, AST, THP-1 activation level and cell viability\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7848619/v1/daf36bb8a02b9610e969606f.png"},{"id":97666193,"identity":"09b85250-cd02-4c63-9045-e941e36858b4","added_by":"auto","created_at":"2025-12-08 09:20:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneration of screen of 28 model-nominated LNP formulations.\u003c/strong\u003e a, Chemical structure of the lipids building blocks: headgroups (R1, a–d), linkers (R2, a–d) and tails (R3, a–d), of ionizable lipid evaluated for delivery. b, General assembly scheme showing stepwise coupling of R1, R2, and R3 to afford tertiary-amine lipids; arrows indicate the order of reactions. The lipids were named Lipid -R1xR2yR3z, where X represents the serial number of amine groups, Y represents the linkers , and Z represents the lipid tails. c, Example chemical structure of lipids R1aR2aR3a and R1bR2bR3d. d, Each vertical bar represents a variant library for one lipid class-ionizable (n=11), helper (n=8), sterol (n=1), PEG (n=7), and additional lipid (n=9). The sequential shades indicate distinct variants within a class. Formulations were assembled combinatorially by drawing from these libraries. e, Component ratios were varied independently and are summarized in the Heatmap.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7848619/v1/ec072b1c30b1ee9b00b60da2.png"},{"id":97404015,"identity":"ff65229e-a9b5-4fd8-a8f7-63803403ca25","added_by":"auto","created_at":"2025-12-04 03:09:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreen of eGFP mRNA containing formulations. a\u003c/strong\u003e, Bar charts of transfection efficiency levels in primary human T-cells, with separate panels for activated (black) and non-activated (gray) cells. Formulations chosen according to model score. \u0026nbsp;B. Screening of formulations in human Peripheral Blood Mononuclear Cells (hPBMC) shows the top performers in activated cells have minimal activity in non-activated cells, and vice versa, yielding largely non-overlapping hit sets with only a small shared subset. C, Venn- diagram illustrate the formulation that acceded the filtration criteria (Activated \u0026gt;60 eGFP %+ , Non-activated \u0026gt; 20 eGFP %) and the minimal shared overlap. D, Non-activated T-cells: top four candidates achieve up to 40 % eGFP expression, compared with \u0026lt;5 % for LP-01 and transfection reagent control (polyplus JetMessenger), Significance is shown only for LP-01. Colors indicate relative performance. E, Activated T-cells: efficiencies reach 90%. Significance is shown only for LP-01.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7848619/v1/e6ee8a5140cf25cc40e63cdd.png"},{"id":97666926,"identity":"9a6bd301-3bc0-4c6a-987e-904641ec9e90","added_by":"auto","created_at":"2025-12-08 09:22:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD19-CAR mRNA introduction using lead LNP lead candidates. \u003c/strong\u003eA CD19 CAR mRNA (GeneScript) transfection in CD3⁺ T-cells using leading LNP candidates showed up to ~25% CAR expression. B, transfection in T-cells subpopulations (CD4⁺, CD8⁺) reached up to ~35%. Data represent mean ± s.d. of n = 3. C. Cytotoxicity assay of CAR-T cells co-cultured with CD19⁺ NALM6 leukemia cells at an effector-to-target (E:T) ratio of 10:1, monitored at 0, 24, 48, and 72 hours. Lead LNP candidates FMB-3199 and FMB-3258 achieved 95% and 98% target cell killing, respectively, by 72 h, compared with 65% killing by the SM-102 LNP control. D, Equivalent assay at a reduced E:T ratio of 5:1 showed 74% (FMB-3199) and 88% (FMB-3258) NALM6 cell killing at 72h, versus 48% for SM-102. In all assays, 10,000 NALM6 target cells were seeded per well. E. Further optimization of lead LNPs produced novel formulations that exhibited enhanced cytotoxicity at an E:T ratio of 5:1, with FMB-4744 and FMB-4745 achieving 97% and 92% target cell elimination, respectively, at 48 hours post- treatment. All comparisons versus UT are significant.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7848619/v1/6944de70169ff45cd666d3f5.png"},{"id":97666941,"identity":"0529dc92-3be2-4df4-8349-538acb65eec6","added_by":"auto","created_at":"2025-12-08 09:22:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":485821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn-vivo transfection of lead LNP candidates in rodents. A. \u003c/strong\u003ePercentage of circulating human T-cells expressing eGFP 24 h after a single 0.65 mg kg⁻¹ dose\u003cstrong\u003e. \u003c/strong\u003eFour candidates reach between 25-55% eGFP expression versus \u0026lt;5 % for LP-01 reference (mean ± s.e.m., n = 5 mice; P \u0026lt; 0.01) Significance is shown only for LP-01. B, FMB-3199 achieve up to 20 % eGFP expression in non-activated T-cells and up to 30 % eGFP expression in activated T-cells. \u003cstrong\u003eC-D\u003c/strong\u003e, Coagulation parameters (aPTT, PT) remain within normal range at 0.5, 0.9 and 1.2 mg kg⁻¹. E-F Hepatic enzymes (AST, ALT) show mild and transient to no elevation. G, Representative H\u0026amp;E liver sections (24 hours post- treatment) reveal only minimal single-cell necrosis and infiltration. e, showed only minimal, non-adverse changes consistent with transient hepatic adaptation.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7848619/v1/3c88de3f866666e1b7edef8a.png"},{"id":97404016,"identity":"04bb477a-4f02-471a-956a-7849e9b89bca","added_by":"auto","created_at":"2025-12-04 03:09:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":275453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-dependent in-vivo transfection and biodistribution of FMB-3199 in non-human primates.\u003c/strong\u003e A, Dose–response transfection efficiency in peripheral CD3⁺ T-cells following intravenous administration of FMB-3199 at 0.5, 0.9, and 1.2 mg kg⁻¹. Transgene expression increased in a dose-dependent manner, reaching ~2.5%, ~8%, and ~15% respectively at 24 h post-injection. B, Transfection efficiency in circulating CD4⁺ and CD8⁺ T-cell subsets at the highest dose (1.2 mg kg⁻¹), reaching ~25% and ~8% respectively. C, Biodistribution profile of FMB-3199 across immune and off-target tissue. Hepatocyte transfection remained minimal (~2%) at all doses, indicating effective immune-targeted delivery and liver avoidance. \u0026nbsp;D-E. ALT and AST remain at baseline following high-dose (1.2 mg/kg) FMB-3199. F-G, Coagulation factors and CBC values stay within physiological limits, H-I, IL-6 and IP-10 exhibit only mild, transient elevations. J, Other measured cytokines (TNF-a, IFN-g, IL1b) are unchanged and remained below level of detection. K-L, Representative H\u0026amp;E micrographs (24 hours post- treatment) show normal hepatic architecture in K, FMB-3199-treated animals versus L, buffer control. FMB-3199 had no evidence of inflammation, necrosis or fibrosis at any dose or time point.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7848619/v1/81a7fa71a7fc1dd2bec8b6e3.png"},{"id":104779422,"identity":"f0057824-2834-4f32-8f49-627e20a98a64","added_by":"auto","created_at":"2026-03-17 07:40:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2147861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7848619/v1/127b98f4-0cad-4467-b2ea-3a5b797b1752.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nN.S., E.Y., I.N., G.R., L.G., M.H., N.R., D.M., N.S., and I.L. are employees of Mana.bio.\r\nA.S. is affiliated with both Mana.bio and the Technion.\r\nAll other authors are affiliated with Sheba Medical Center and have no competing financial interests.","formattedTitle":"Machine Learning-Driven Discovery of a Lipid Nanoparticle for In-Vivo T-Cell Transfection in Non-Human Primates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLipid nanoparticles (LNPs) have revolutionized the field of nucleic acid delivery, emerging as the vehicle of choice for therapeutic payloads such as mRNA, siRNA, and gene-editing machinery​\u003csup\u003e1\u003c/sup\u003e​. Their prominence was catapulted into the global spotlight during the COVID-19 pandemic, where LNP-encapsulated mRNA vaccines enabled rapid, scalable, and safe immunization strategies​\u003csup\u003e2\u003c/sup\u003e​. LNPs offer a non-viral, highly tunable, and clinically validated alternative with proven performance across multiple therapeutic modalities​\u003csup\u003e3\u003c/sup\u003e​.\u003c/p\u003e\u003cp\u003eA critical frontier in therapeutic delivery is the efficient transfection of immune cells, especially T lymphocytes​\u003csup\u003e4\u003c/sup\u003e​. These cells are central orchestrators of adaptive immunity and hold transformative promise in engineered cell therapies such as chimeric antigen receptor T-cell (CAR-T) therapy​\u003csup\u003e5\u003c/sup\u003e​. CAR-T therapies have demonstrated curative potential in hematologic malignancies, offering hope to patients with refractory or relapsed cancers​\u003csup\u003e6\u003c/sup\u003e​. However, the current ex-vivo CAR-T manufacturing paradigm is laborious, time-intensive, and costly, often requiring weeks of autologous cell processing under Good Manufacturing Practice (GMP) conditions, followed by lymphodepleting chemotherapy to facilitate engraftment​\u003csup\u003e7\u003c/sup\u003e​.\u003c/p\u003e\u003cp\u003eIn contrast, an in-vivo CAR-T approach, wherein T-cells are directly engineered within the patient using systemically delivered genetic payloads, offers a paradigm shift. It bypasses complex manufacturing, reduces cost and time to treatment, and potentially eliminates the need for preconditioning regimens​\u003csup\u003e6\u003c/sup\u003e​. Achieving this, however, requires the development of delivery systems that can transfect non-activated T-cells in circulation with high efficiency, specificity, and safety-a feat that has eluded the field due to the intrinsic resistance of resting T-cells to nucleic acid uptake​\u003csup\u003e4\u003c/sup\u003e​.\u003c/p\u003e\u003cp\u003eThe lipid chemical space is vast and highly modular, comprising a near-infinite array of possible combinations of headgroups, tails, linkers, and functional motifs​\u003csup\u003e8\u003c/sup\u003e​. Traditionally, LNP development has relied heavily on trial-and-error, empirical formulation, and expert-driven intuition-a laborious and inefficient process that struggles to explore the full combinatorial landscape of possible candidates​\u003csup\u003e9\u003c/sup\u003e​. In recent years, ML has emerged as a powerful tool for hypothesis-free design across multiple domains, from protein folding and molecular dynamics to clinical diagnostics and materials science​\u003csup\u003e10\u003c/sup\u003e​. In the context of nanoparticle development, ML offers the potential to quickly navigate the chemical space, identify high-performing formulations, and optimize for complex, multi-dimensional criteria such as potency, selectivity, and safety​\u003csup\u003e11\u003c/sup\u003e​.\u003c/p\u003e\u003cp\u003eIn this study, we demonstrate the power of a machine-learning-guided lipid nanoparticle design platform to achieve efficient in-vivo transfection of T-cells in-vitro, mice and non-human primates within a six-month development timeline. Our approach leverages a data-rich DBTL framework that integrates literature-derived and proprietary datasets, high-throughput experimental feedback, and predictive modeling. We identify FMB-3199 as a safe, stable, and effective LNP capable of delivering mRNA to CD3⁺, CD4⁺, and CD8⁺ T-cells with minimal liver transfection and favorable toxicity profile. This work establishes a foundation for rapid, in-vivo engineering of immune cells and opens a pathway toward scalable, patient-friendly T-cell therapies for oncology and autoimmune indications.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMachine Learning Model Development and Foundational Dataset Construction\u003c/p\u003e\u003cp\u003eTo establish a strong model foundation, we first curated a comprehensive public-domain dataset of lipid nanoparticle (LNP) formulations for T-cell transfection from the literature and patents, comprising over 1,500 datapoints describing transfection efficiency, viability, and physico-chemical properties. With each cycle of DBTL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), the model was iteratively refined using proprietary wet-lab data: ~1,500 transfection datapoints, ~\u0026thinsp;1,500 viability measurements, ~\u0026thinsp;3,500 physico-chemical (PC) descriptors, and ~\u0026thinsp;4,000 safety datapoints, including THP-1 activation, Alanine Aminotransferase (ALT), Aspartate Aminotransferase (AST) and cytokine release (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). These additions increased both model accuracy and formulation diversity, as evidenced by the progressive data accrual and steady increase in top predictive performance across cycles. Using this dataset, we trained two initial machine learning (ML) models to predict transfection efficiency and cell viability of LNPs in non-activated human T-cells. The transfection model achieved a mean absolute error (MAE) of 3.58 and Pearson correlation of 0.79, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec. The viability model achieved MAE of 13.71 and Pearson correlation of 0.72 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eSafety is a crucial aspect in LNP formulations, and to incorporate it into our screening process we developed predictive models for key safety-related assays, including ALT, AST, THP-1 activation, and cell viability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). These models improved steadily over time through weeks of iterative DBTL cycles. Each week, newly generated wet-lab data from novel formulations were added to the database, and the model produced predictions for all four assays. The predictions were then validated experimentally, and correlation scores between predicted and observed values were calculated to quantify performance. These results were fed back into the database to refine the models in the subsequent iteration. With weekly data integration and iterative retraining, all models showed progressive and consistent gains in predictive accuracy, highlighting the importance of feedback-driven refinement for reliable LNP safety screening.\u003c/p\u003e\u003cp\u003eIn Vitro Proof-of-Concept and LNP Formulation Screening\u003c/p\u003e\u003cp\u003eTo ensure that the testing system operates effectively, we assembled an LNP library that not only included our proprietary novel formulations but also rationally selected immune-active molecules (e.g., MB-5132), known formulations used as controls (MC3, SM-102 and LP-01) and literature-reported lipids previously explored for T-cell transfection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The general assembly and naming scheme are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c. Candidate head-linker-tail combinations were scored in silico and ranked by our model.Variant libraries were constructed for each lipid class and combinatorially assembled (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Formulations were then generated by independently varying component ratios, ranked by our model, and the top set was selected. Compositions of the top set and controls are summarized in the formulation ratios heat map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Top-ranked candidates and controls were synthesized and tested in primary human T-cells (activated and non-activated) in collaboration with Sheba Medical Center. Flow cytometry analysis showed that several candidates enabled significant transfection in both activated and non-activated T-cells, with model-nominated formulations performing notably better than the control - polyplus JetMessenger 5k (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eModel-Driven Iterations and hPBMC Optimization\u003c/p\u003e\u003cp\u003eFollowing the initial screen, DBTL cycles transitioned to hPBMCs, enabling more scalable and diverse profiling. After each round, transfection and viability results were fed back into the model to retrain and reprioritize future candidates.\u003c/p\u003e\u003cp\u003eIn these hPBMC studies, in-silico formulation design differed substantially depending on activation state; Formulations that ranked highly in activated T-cells generally showed limited response in non-activated cells, and vice versa, yielding largely non-overlapping performance profiles with only a small, shared subset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Because non-activated cells are harder to transfect, evaluation criteria were adjusted for each state and included as a feature across the models (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). This data indicates that success factors differ between activated and non-activated T-cells which perhaps argues for state-specific optimization.\u003c/p\u003e\u003cp\u003eLead-candidate LNPs support potent eGFP and CAR mRNA delivery across T-cell subsets\u003c/p\u003e\u003cp\u003eTo identify lead formulations capable of efficient T-cell transfection, we conducted a series of DBTL optimization cycles in hPBMCs. In non-activated T-cells-considered one of the most challenging targets for mRNA delivery-the top four machine learning-nominated lipid nanoparticles (LNPs) achieved between 30 to 40% eGFP expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). This performance represented a\u0026thinsp;\u0026gt;\u0026thinsp;10-fold improvement relative to the benchmark ionizable lipid LP-01 and the commercial transfection reagent Polyplus JetMessenger 5k, both of which failed to exceed 5% efficiency in this setting. Activated T-cells exhibited higher permissiveness to LNP-mediated delivery, with candidates achieving 70\u0026ndash;90% eGFP expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), except FMB-3999 which performed best in non-activated T-cells, establishing a clear potency gradient dependent on activation status.\u003c/p\u003e\u003cp\u003eTo evaluate the translational potential of these LNPs for therapeutic payloads, CD19 chimeric antigen receptor (CAR) mRNA (GeneScript) was formulated into high-ranked candidates in parallel with eGFP formulations, and transfection efficiency was assessed in CD3⁺ T-cells. These experiments demonstrated successful delivery of functional CAR mRNA, with expression rates reaching\u0026thinsp;~\u0026thinsp;25% in CD3⁺ cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Further dissection of transfection across T-cell subsets revealed peak CAR expression levels of ~\u0026thinsp;35% in CD4⁺ T-cells and substantial transfection in CD8⁺ populations as well (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Across all conditions, transfection was achieved without compromising cell viability, and results were consistent across biological replicates (n\u0026thinsp;=\u0026thinsp;3 donors). These data confirm the ability of ML-optimized LNPs to robustly deliver both reporter and functional therapeutic mRNA into key immune cell subsets, highlighting their potential for in-vivo engineering of T-cells in cancer and autoimmune settings.\u003c/p\u003e\u003cp\u003eTo validate the activity of the LNP-delivered CAR mRNA, we performed cytotoxicity assays against NALM6 leukemia B cells. At an effector-to-target (E:T) ratio of 10:1, two lead formulations (FMB-3199 and FMB-3258) showed robust activity, reaching 95% and 98% target-cell killing by 72h respectively, compared with 65% for the SM-102 control (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). At an E:T ratio of 5:1, the same formulations achieved 74% and 88% killing by 72h respectively, again outperforming the control (48%; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In a subsequent optimization step, we evaluated additional formulations which are FMB-3199 analogs, modifying the helper lipid component while keeping the rest of the formulation identical. Thereby identified two analogs that surpassed FMB-3258, yielding up to 97% in target-cell elimination at 48h, E:T ratio of 5:1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003eIn Vivo Validation in Humanized Mice and Dose-Finding in Rats\u003c/p\u003e\u003cp\u003eTo validate performance in vivo, female NSG mice were injected with 20M hPBMCs followed by intravenous LNP administration (0.65 mg/kg). After five DBTL cycles, four lead candidates achieved between 25\u0026ndash;55% transfection in circulating human T-cells-substantially higher than the LP-01 reference (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). FMB-3199, which in vitro showed expression largely restricted to activated T-cells, displayed a broader in vivo profile of ~\u0026thinsp;20% eGFP positivity in non-activated s and ~\u0026thinsp;30% in activated s (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThe DRF study in rats (females, n\u0026thinsp;=\u0026thinsp;3 rats per group) defined the safety profile of our three lead LNP candidates. Dose escalation to 0.5, 0.9, and 1.2 mg/kg was well tolerated: liver enzymes, cytokines, and coagulation parameters remained stable over 24 hours post-dose, and liver histology showed no treatment-related abnormalities, supporting hepatic tolerability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-h).\u003c/p\u003e\u003cp\u003eFormulation Stability and Freeze-Thaw Performance\u003c/p\u003e\u003cp\u003eSix lead formulations were tested for size, polydispersity index (PDI), and encapsulation efficiency (EE) before and after dialysis and freeze-thaw process. FMB-3199 showed great stability, retaining\u0026thinsp;~\u0026thinsp;97 nm size, PDI\u0026thinsp;\u0026lt;\u0026thinsp;0.07, EE\u0026thinsp;\u0026gt;\u0026thinsp;84%, and ~\u0026thinsp;80% transfection efficiency activity post-thaw.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ephysico-chemical stability of lead formulations. Particle size, polydispersity index (PDI) and encapsulation efficiency (EE %) measured immediately after formulation, post-dialysis and after one freeze-thaw cycle. FMB-3199 maintains\u0026thinsp;~\u0026thinsp;96 nm diameter, PDI\u0026thinsp;\u0026asymp;\u0026thinsp;0.06 and EE\u0026thinsp;\u0026gt;\u0026thinsp;84 %, indicating preserved physicochemical characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"19\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eDirect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u003cp\u003eDialysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e\u003cp\u003eThaw\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFMB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSize (nm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eEE (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eSize (nm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003ePDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eEE (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eSize (nm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003ePDI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003eEE (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMB-3999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e108.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e84.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e110.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e80.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMB-3258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c14\"\u003e\u003cp\u003e108.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e87.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMB-3875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e123.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e93.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e123.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e96.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMB-3876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e85.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e89.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e82.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMB-3879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e97.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e86.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMB-3199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e86.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e98.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e90.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNon-Human Primate Studies Validated FMB-3199 as Lead Candidate\u003c/p\u003e\u003cp\u003eFMB-3199 was evaluated in NHPs across a dose range of 0.5, 0.9, and 1.2 mg/kg to assess its transfection efficiency, biodistribution, and preliminary safety. A robust and reproducible dose-response was observed in circulating CD3⁺ T-cells, with transfection efficiencies of ~\u0026thinsp;2.5% at the low dose, ~\u0026thinsp;8% at the intermediate dose, and ~\u0026thinsp;15% at the highest tested dose (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). This linear trend underscores the formulation\u0026rsquo;s pharmacological scalability and supports dose titration for future clinical settings. This trend extended to T-cell subsets where transfection in CD4⁺ T-cells was particularly pronounced, reaching up to ~\u0026thinsp;25% at the 1.2 mg/kg dose, while CD8⁺ T-cells exhibited\u0026thinsp;~\u0026thinsp;8% transfection, suggesting differential uptake and/or expression dynamics across T-cell compartments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eImportantly, off-target hepatic expression was minimal across all dose levels, with transgene signal restricted to ~\u0026thinsp;2% of hepatocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). This low hepatic tropism contrasts with conventional LNPs such as SM-102 and MC3, which show strong liver enrichment\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Along with reduced hepatic uptake, B-cells delivery reached\u0026thinsp;~\u0026thinsp;7%, reflecting the success of our ML-guided design strategy aimed at immune-cell selectivity and liver avoidance.\u003c/p\u003e\u003cp\u003eToxicity panels over 24 hours after high dose with FMB-3199showed mildly elevated liver enzymes, transient mild elevation in 2/5 cytokines, and no abnormalities in coagulation or CBC, consistent with a benign and well-tolerated safety profile across parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed-j).\u003c/p\u003e\u003cp\u003eLiver histopathology confirmed no adverse findings. Hematoxylin and eosin (H\u0026amp;E) staining of liver sections from FMB-3199-treated non-human primates revealed intact hepatic lobular architecture, with no signs of necrosis, inflammatory infiltrates, fibrosis, or bile duct injury. Across all tested doses, liver morphology remained indistinguishable from vehicle-treated controls, supporting the overall safety and hepatic tolerability of the formulation (Fig.\u0026nbsp;9k,l).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings establish FMB-3199 as a highly safe, and stable LNP candidate for future applications in T-cell engineering and mRNA-based immunotherapies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCAR T-cell therapy has produced remarkable outcomes across several indications. Nonetheless, most approaches still require ex vivo modification which carries safety concerns and demands long, costly manufacturing​\u003csup\u003e7\u003c/sup\u003e​. Therefore, transfecting T- cells in vivo holds great potential for an alternative therapeutic approach​\u003csup\u003e3\u003c/sup\u003e​. However, identifying safe, effective in vivo delivery systems and conditions remains challenging. This complexity makes traditional trial-and-error methods too slow and costly​\u003csup\u003e12\u003c/sup\u003e​. In this study we demonstrate the power and potential of AI-guided workflow, to accelerate discovery. Initially, we compiled literature readouts into a dataset suitable for modeling; we then applied an ensemble of predictors in a DBTL workflow to nominate LNPs and evaluated them in vitro in-house. The results were integrated into the model's training set to improve its predictive accuracy. With this strategy as a process, we nominated a selection of novel candidates that exceeded the benchmark both in vitro and in vivo.\u003c/p\u003e\u003cp\u003eA central hurdle for in-vivo T-cell engineering is that efficient delivery typically improves with cell activation, but couples to rapid expansion​\u003csup\u003e14\u003c/sup\u003e​. Transfecting non-activated T-cells, which exhibit superior differentiation characteristics and reduced exhaustion, poses a greater delivery challenge​\u003csup\u003e14\u003c/sup\u003e​. Therefore, both cell states remain viable targets, with distinct trade-offs. Hence, from the very first screen we evaluated the formulation\u0026rsquo;s efficiency in both activated and non-activated hPBMC T-cells, and the T-cell state was introduced to the model as a feature.\u003c/p\u003e\u003cp\u003eBy leveraging rational design and model-based screening, we were able to identify LNPs capable of preferentially transfecting either activated or non-activated cells, as well as formulations with balanced activity across both states. This flexibility constitutes a strategic advantage, as it enables the tailoring of LNP formulations to distinct therapeutic indications-whether favoring activated T-cells for rapid immune responses, non-activated T-cells for long-lived persistence, or a combination of both for broader efficacy. Moreover, our platform highlights LNPs optimized for ex-vivo transfection of non-activated T-cells, enabling CAR-T manufacturing with minimal activation while preserving viability. This reflects the incorporation of domain knowledge into the AI pipeline via expert-guided feature selection and state-aware objectives, yielding fit for purpose candidates.\u003c/p\u003e\u003cp\u003eTranslating our findings in vivo, we conducted five DBTL cycles in NSG mice using both activated and non-activated hPBMC T-cells. FMB-3199, a newly proposed candidate, surpassed the in-vitro transfection threshold and demonstrated superior transfection rates compared to other LNP candidates, achieving approximately 20% in non-activated cells and 30% in activated cells. Moreover, in NHPs, FMB-3199 maintained cross-species performance, with transfection increasing with dose in both circulating CD3⁺ T-cells and their subsets.\u003c/p\u003e\u003cp\u003eYet another concern is that CAR-T gene-therapy vectors must specifically target T-cells while minimizing off-target uptake and transfection, as these can pose safety risks​\u003csup\u003e15\u003c/sup\u003e​. This is a primary concern for LNPs, which exhibit strong hepatic tropism, which can result in dose-limiting hepatotoxicity if liver exposure is not adequately controlled​\u003csup\u003e16\u003c/sup\u003e​.\u003c/p\u003e\u003cp\u003eThe safety parameters evaluated in NHPs are not only critical indicators of tolerability in preclinical studies but also directly relevant to the clinical advancement of in vivo CAR-T platforms​\u003csup\u003e20\u003c/sup\u003e​. Liver enzymes such as ALT and AST serve as sensitive markers of hepatocellular stress​\u003csup\u003e18\u003c/sup\u003e​; their mild transient changes in FMB-3199-treated animals demonstrates that the formulation avoids the hepatotoxicity commonly observed with conventional LNPs​\u003csup\u003e17\u003c/sup\u003e​. This is particularly important for clinical translation, where repeated dosing may be required​\u003csup\u003e19\u003c/sup\u003e, and hepatic safety often defines the upper bound of the therapeutic window​\u003csup\u003e20\u003c/sup\u003e​. Cytokine monitoring provides insight into systemic immune activation, as excessive release of pro-inflammatory mediators can lead to cytokine release syndrome (CRS)-a major clinical concern in CAR-T therapy​\u003csup\u003e21\u003c/sup\u003e​. The observation that key cytokines (IP10, IL6, IFN-g, TNF-a and IL1b) remained at baseline or showed transient mild elevation, supports the notion that FMB-3199 engages T-cells without triggering pathological systemic inflammation. Finally, coagulation profiling is essential, as several nucleic acid-based therapies have been associated with thrombogenicity​\u003csup\u003e22\u003c/sup\u003e​; maintaining physiological coagulation parameters in NHPs suggests a low risk of infusion-related coagulopathies in humans​\u003csup\u003e23\u003c/sup\u003e. Taken together, these safety readouts provide mechanistic reassurance that FMB-3199 combines efficient T-cell targeting with a benign systemic safety profile, thereby strengthening confidence in its suitability for in-vivo CAR-T applications and its potential to advance safely into the clinic.\u003c/p\u003e\u003cp\u003eFor clinical translatability, it is essential to evaluate LNPs with the actual therapeutic payload​\u003csup\u003e24\u003c/sup\u003e​. Accordingly, we confirmed efficient in-vitro delivery of CD19 CAR mRNA to CD3⁺ T-cells and their CD4⁺/CD8⁺ subsets using our lead LNP which retained its delivery and functional properties upon payload exchange, as evidenced by robust CD19-directed killing. Given the favorable safety profile of FMB-3199, we then examined closely related structural analogs designed to enhance performance while preserving safety. Guided by our ML models, these analogs demonstrated improved activity, achieving\u0026thinsp;~\u0026thinsp;98% killing in co-culture assays.\u003c/p\u003e\u003cp\u003eThe favorable balance between efficacy and safety observed in NHPs positions FMB-3199 as a promising candidate for further clinical development. The linear dose-response in T-cell transfection, combined with minimal hepatic off-target expression and an excellent tolerability profile, suggests that higher doses could be explored to further increase transfection efficiency without compromising safety. In particular, the absence of hepatotoxicity, stable liver histopathology, and only transient cytokine changes across the tested range indicate a wide therapeutic window for this formulation. Thus, beyond demonstrating proof-of-concept, these findings support the potential for dose escalation strategies aimed at achieving enhanced T-cell modification rates, while maintaining the benign safety profile that distinguishes FMB-3199 from conventional LNP platforms.\u003c/p\u003e\u003cp\u003e In summary, we demonstrate how an AI-guided, closed-loop workflow (we need to show an example of how the molecules and formulations change with between the cycles) can automate and elevate experimental design, data integration, and analysis. By fusing existing literature, in-house datasets, and predictive models within rapid design-build-test-learn cycles, we efficiently explored novel lipids and identified a lead LNP with selective T-cell transfection and a favorable safety window in vivo, supporting its therapeutic potential and paving the way for further preclinical development.\u003c/p\u003e"},{"header":"Methods \u0026 Materials","content":"\u003cp\u003eLipid synthesis and formulation\u003c/p\u003e\n\u003cp\u003eLipid synthesis:\u003c/p\u003e\n\u003cp\u003eLipids were synthesized according to the general scheme (Figure 2b). Ionizable lipids were synthesized through epoxide ring opening or Michael addition with a polyamine core under heat in ethanol followed by silica purification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFormulations were assembled from Lipids and aqueous phase. The aqueous phase was prepared by mixing eGFP mRNA (GenScript, custom sequence) with citrate buffer (200 mM, pH 4.9-5.1) which was prepared by dissolving citric acid (Bio-Lab, #000302059100) and trisodium citrate (Fisher Chemicals, #10396430), followed by sterile filtration through a 0.22 \u0026mu;m membrane, and molecular-grade water (Sartorius, #01-869-1A) according to N:P 4 calculation. Lipids were dissolved in ethanol, heated for 10 min, and combined into formulation-specific mixtures, which were reheated at 55 \u0026deg;C for 10 min. Lipid and aqueous phases were loaded into syringes and combined using a microfluidics chip (Cytiva, #NIN0062) operated with the NanoAssemblr program at a total flow rate of 12 mL/min.\u003c/p\u003e\n\u003cp\u003eFormulations were buffer-exchanged into a cryoprotective solution (molecular-grade water, 1% Tris-HCl [0.5 M, Thermo, #J67501.AK], 12.5% sucrose [JT Baker, #4072]) by overnight dialysis at 4\u0026deg;C using a 20 kDa Float-A-Lyzer (Merck, Z726931-12EA). Samples were cryopreserved in 1.8-mL cryovials at \u0026minus;80 \u0026deg;C until use. Physicochemical characterization of LNPs was performed both post-dialysis and after thawing to confirm LNPs integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 | The ionizable-lipid library was used for model-based ranking. The model-selected top set was subsequently evaluated in vitro.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"416\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eDLin-MC3-DMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003ePEG 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eDMG-PEG2000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003ec12-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003ePEG 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003ec14-peg2000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eR1bR2bR3d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003ePEG 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eDSG-PEG2000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eR1dR2aR3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003ePEG 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eDSPE-PEG2000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003ecKK-E12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003ePEG 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eALC-0159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eALC-0315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003ePEG 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eBrij S20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eR1bR2bR3c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003ePEG 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eMB-5035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eSM-102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eAdditional lipid 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eMB-5132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eR1cR2bR3c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eAdditional lipid 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eVitamin-D2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eR1dR2bR3d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eAdditional lipid 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eVitamin-D3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eIonIzable 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eR1aR2aR3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eAdditional lipid 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eMB-5057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eDOPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eAdditional lipid 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eMB-5112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eDSPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eAdditional lipid 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eDOTAP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eSOPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eAdditional lipid 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003eMB-5024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eTriolein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eDOPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eDiolein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eMB-5118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003eHelper 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.0865%;\"\u003e\n \u003cp\u003eMB-5132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.28846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.3558%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.9135%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ePhysicochemical characterization\u003c/p\u003e\n\u003cp\u003eFormulations were diluted to 20 \u0026micro;g/mL in PBS (Sartorius, #02-023-1A). Particle size and polydispersity index (PDI) were determined either by diluting 50 \u0026micro;L of formulation in 450 \u0026micro;L PBS and measuring by dynamic light scattering (Zetasizer Nano ZSP, Malvern Instruments), or by diluting 5 \u0026micro;L in 800 \u0026micro;L PBS and dispensing 100 \u0026micro;L per well in triplicate into clear 96-well plates for analysis on the Dynapro.\u003c/p\u003e\n\u003cp\u003eEncapsulation efficiency (EE%) was assessed with the Quant-iT RiboGreen assay (Thermo Fisher Scientific, R11490). Briefly, 5 \u0026micro;L of formulation was dispensed in triplicate into black 96-well plates, followed by 45 \u0026micro;L TE buffer and 50 \u0026micro;L RiboGreen (1:100). After 5 min at 37\u0026deg;C, fluorescence corresponding to unencapsulated RNA was measured (Tecan plate reader). Samples were then lysed with 50 \u0026micro;L 2% Triton (Sigma, #102671448) and re-read to determine total RNA. EE% was calculated as\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"374\" height=\"72\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eZeta potential was measured after diluting 100 \u0026micro;L of formulation in 1.9 mL 10% sucrose (Sigma, #0389-1KG, pH 7.20-7.45) using a DTS1070 cuvette on the Zetasizer Nano ZSP. Apparent pKa was determined using the TNS assay (Abcam, #ab275049): formulations were incubated in 12 buffers spanning a pH gradient, followed by addition of TNS dye. Fluorescence was recorded (Tecan plate reader with automated liquid handler), normalized as (fluorescence \u0026minus; minimum)/(maximum \u0026minus; minimum), and fitted to a sigmoidal titration curve. The pKa was defined as the pH at half-maximal fluorescence.\u003c/p\u003e\n\u003cp\u003eCell culture and in vitro assays\u003c/p\u003e\n\u003cp\u003eA549 cells were maintained in Complete medium). Cells (2 \u0026times; 10⁴ per well) were seeded in white 96-well plates and treated in triplicate with test formulations (0.5, 1, or 3 \u0026micro;g/mL). After 24 h, viability was assessed using the CellTiter-Fluor\u0026trade; assay (Promega, #G6082).\u003c/p\u003e\n\u003cp\u003eTHP-1 NF-\u0026kappa;B reporter cells were cultured with complete medium (RPMI 1640 medium (Sartorius, #01-100-1A) supplemented with 10% FBS (Gibco, #A5256801), 1% penicillin-streptomycin (Biowest, #L0022-100), and 1% L-glutamine (Sartorius, #03-020-1A)s, supplemented with 25 mM HEPES (Sartorius, #03-025-1B) and 100 \u0026micro;g/mL zeocin (InvivoGen, #ant-zn-1), and maintained up to passage 20. Cells (2.5 \u0026times; 10⁴ per well) were seeded into 96-well plates and treated with formulations (0.5, 1, or 3 \u0026micro;g/mL). LPS (1 \u0026micro;g/mL; Merck, #L2387-10MG) served as a positive control. After 48 h, alkaline phosphatase activity was measured by transferring 20 \u0026micro;L supernatant into 180 \u0026micro;L Quanti-Blue\u0026trade; reagent (InvivoGen, #rep-qbs2) and reading OD at 620 nm. Reporter activity was normalized to viability (CellTiter-Fluor\u0026trade;).\u003c/p\u003e\n\u003cp\u003ehPBMCs\u003c/p\u003e\n\u003cp\u003eHuman peripheral blood mononuclear cells (PBMCs) were obtained from whole blood (Magen David Adom, Israel) by density gradient separation with Lymphoprep\u0026trade; (Stemcell Technologies, #18061) following dilution (1:1 with PBS) and centrifugation at 800 \u0026times; g for 20 min (brakes off). The PBMC layer was collected, washed, and cryopreserved in NutriFreez\u0026reg; (Sartorius, #05-713-1A). PBMCs either isolated in-house or obtained from \u0026lsquo;CellGeneration\u0026rsquo;, were thawed and seeded into culture flasks at a density of 1 \u0026times; 10⁶ cells/mL in complete medium (as described above). For activation, PBMCs were stimulated with TransAct (Miltenyi Biotec, 130-111-160) at a 1:100 dilution and recombinant human IL-2 (PeproTech, #200-02) at a final concentration of 50 ng/mL. Activated PBMCs were cultured for 72 hours prior to plating for treatment, whereas non-activated PBMCs were incubated overnight. Cells were seeded at a density of 1 \u0026times; 10⁶ cells/mL and treated for 20 h with either GFP mRNA (3 \u0026micro;g/mL) or CD19 CAR-T mRNA (6 \u0026micro;g/mL) formulations. Following incubation, cells were washed and analyzed by flow cytometry (FC) or subjected to cytotoxicity assays. JetMessenger transfection reagent (Polyplus, #101000056) was used as a positive control for flow cytometry, while untreated cells served as negative controls for both FC and cytotoxicity assays. Culture supernatants were collected and stored at \u0026minus;20 \u0026deg;C for subsequent ELISA analysis.\u003c/p\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003cp\u003eCytokine concentrations in cell culture supernatants were quantified using ELISA kits (R\u0026amp;D Systems; human IL-6, #D6050; human TNF-\u0026alpha;, #DTA00D; human IFN-\u0026gamma;, #DIF50C) according to the manufacturer\u0026rsquo;s instructions\u003c/p\u003e\n\u003cp\u003eFlow Cytometry\u003c/p\u003e\n\u003cp\u003eCells were washed with PBS and incubated with a viability dye for 30 minutes at room temperature. Following additional PBS wash, cells were treated with Fc block for 15 minutes and subsequently stained with fluorophore-conjugated antibodies specific for the desired markers. Cells were washed again with FACS buffer - PBS 0.5% BSA (SIGMA, #03117057001) 0.5mM EDTA (JT Baker, #8993-01) Samples were either acquired immediately on a Northern Lights flow cytometer (Cytek) or fixed prior to acquisition. FC data were analyzed using FlowJo software (BD Biosciences).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVendor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCatalog Number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eBlocking reagent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eHuman Fc block (TruStain FcX)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eBioLegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e422302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-mouse CD4⁺5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eMiltenyi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e130-110-798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-mouse Ter119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eMiltenyi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e130-112-910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD3⁺\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eMiltenyi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e130-129-580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD4⁺\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eBioLegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e300539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD8⁺\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eMiltenyi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e130-110-680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eBioLegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e300644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eMiltenyi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e130-112-805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eMiltenyi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e130-131-840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eBioLegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e362578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eBioLegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e301828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-human CD19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eBioLegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e302230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-FMC63 FITC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eAcroBiosystems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003eFM3-FY45-25tests\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5931%;\"\u003e\n \u003cp\u003eAntibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.7381%;\"\u003e\n \u003cp\u003eAnti-FMC63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9341%;\"\u003e\n \u003cp\u003eMiltenyi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7348%;\"\u003e\n \u003cp\u003e130-127-344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKilling assay\u003c/p\u003e\n\u003cp\u003eFor cytotoxicity assays, NALM6-luciferase cells were maintained in complete medium (described above). Cells (1 \u0026times; 10⁴/well) were seeded into white 96-well plates and co-cultured with treated PBMCs at a 5:1 effector-to-target ratio for 48 h. Luminescence was quantified using ONE-Glo (Promega, #E6110) on a Tecan plate reader. Killing was normalized to untreated PBMC controls.\u003c/p\u003e\n\u003cp\u003eIn vivo studies\u003c/p\u003e\n\u003cp\u003eNSG mice (8-10 weeks; Jackson Laboratory) were obtained via Vivox. Mice were injected intravenously with 2 \u0026times; 10⁷ human PBMCs, and 24 h later received formulations at 0.65 mg/kg. Blood was collected 24 h post-treatment; mice were then euthanized, and organs harvested for FC. All procedures were approved by the institutional animal ethics committee.\u003c/p\u003e\n\u003cp\u003eFor dose-range finding (DRF), Sprague-Dawley rats (Jackson Laboratory) received 0.5, 0.9, or 1.2 mg/kg via Vivox. Blood was collected at 1, 6, and 24 h for serum chemistry, coagulation, cytokines, and CBC. Rats were euthanized, and livers processed for histology.\u003c/p\u003e\n\u003cp\u003eFor PBMC isolation from mouse blood, samples were diluted to 2 mL with PBS. RBC lysis was performed with 5 mL of 1\u0026times; RBC lysis buffer (BD, #555899) for 10 min at RT, quenched with RPMI + 10% FBS, and centrifuged at 300 \u0026times; g for 5 min. Lysis was repeated up to three times if erythrocytes remained. Cells were resuspended in PBS and stained for FC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHP study design and animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeven animals were enrolled to evaluate two distinct LNP formulations encapsulating an eGFP mRNA payload. Animals were randomly assigned to receive escalating intravenous (IV) doses of either formulation across three consecutive treatment days. All procedures were conducted in compliance with relevant ethical regulations for animal research.\u003c/p\u003e\n\u003cp\u003eFormulations and dosing regimen\u003c/p\u003e\n\u003cp\u003eAnimals received formulation FMB-3199. The dosing schedule for FMB-3199 was as follows: 0.9 mg/kg on Day 1, 0.5 mg/kg on Day 2, and 1.2 mg/kg on Day 3. For the final administration, animals were pretreated 1h prior to infusion with dexamethasone (1 mg/kg, IV), famotidine (0.5 mg/kg, IV), and diphenhydramine (5 mg/kg, IV). All injections were administered as a continuous IV infusion over 1h.\u003c/p\u003e\n\u003cp\u003eBlood collection and cellular assays\u003c/p\u003e\n\u003cp\u003ePeripheral blood was collected at baseline (pre-dose),1h, 6h, and 24h post-infusion. T-cells and B-cells were isolated and assayed for eGFP expression to assess transfection efficiency. In addition, clinical chemistry, complete blood counts (CBC), coagulation and cytokines panels were performed at all collection timepoints.\u003c/p\u003e\n\u003cp\u003eTissue collection and histology\u003c/p\u003e\n\u003cp\u003eAt 24h following the final administration, liver biopsies were collected. Tissues were fixed in 10% neutral buffered formalin, embedded in paraffin, sectioned, and stained with hematoxylin and eosin for histopathological evaluation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHou, X., Zaks, T., Langer, R. \u0026amp; Dong, Y. 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Lipid nanoparticles (LNPs) offer an in vivo, non-viral alternative to ex-vivo genetic engineering but have historically shown poor performance in T-cells​. Our machine learning approach enabled the rapid design of novel LNPs, seamlessly integrating in-silico prediction with wet-lab validation to accelerate the discovery and optimization process​.\u003c/p\u003e\u003cp\u003eHere, we report the machine learning (ML)-guided discovery of FMB-3199, a passively targeted LNP capable of safe in vivo T-cell delivery without surface-conjugated ligands or antibodies, identified through iterative design-build-test-learn (DBTL) cycles that progressively refined and improved the model\u0026rsquo;s predictive quality. In NSG mice injected with human peripheral blood mononuclear cells (hPBMCs), FMB-3199 achieved\u0026thinsp;~\u0026thinsp;60% transfection of human T-cells in vivo, further validating its translational potential. In addition, its analogs achieved up to 98% killing of NALM6 cells within 48 hours in vitro, underscoring their functional therapeutic efficacy. Finally, in non-human primates (NHPs), FMB-3199 enabled dose-dependent safe CD3⁺ T-cell transfection (~\u0026thinsp;2.5\u0026ndash;15%), with ~\u0026thinsp;25% in CD4⁺ T-cells while minimizing liver uptake.\u003c/p\u003e\u003cp\u003eTogether, these findings establish a scalable and generalizable platform for in-vivo T-cell engineering, accelerating the development of next-generation mRNA-based cell therapies.\u003c/p\u003e","manuscriptTitle":"Machine Learning-Driven Discovery of a Lipid Nanoparticle for In-Vivo T-Cell Transfection in Non-Human Primates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 03:09:11","doi":"10.21203/rs.3.rs-7848619/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":"f75c6cb2-af54-4b16-ad77-08ab77d2b6d5","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57873127,"name":"Biological sciences/Immunology/Immunotherapy"},{"id":57873128,"name":"Biological sciences/Biotechnology/Gene delivery"}],"tags":[],"updatedAt":"2026-03-04T16:37:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 03:09:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7848619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7848619","identity":"rs-7848619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}