Advancing clinical outcome predictions via incorporating pharmacokinetic simulations into in vitro testing - a colorectal cancer example

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Poloznikov , Ben R. Britt , Sergey Nikulin , Sergey Rodin , Karl-Henrik Grinnemo , Martin Woywod , Jasmin Farouq doi: https://doi.org/10.1101/2025.07.29.663668 Andrey A. Poloznikov a Mimi-Q GmbH , Potsdam Science Park 1, Am Mühlenberg 11, 14476 Potsdam, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: andrey{at}mimi-q.com Ben R. Britt a Mimi-Q GmbH , Potsdam Science Park 1, Am Mühlenberg 11, 14476 Potsdam, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sergey Nikulin a Mimi-Q GmbH , Potsdam Science Park 1, Am Mühlenberg 11, 14476 Potsdam, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sergey Rodin b Cardio-Thoracic Translational Medicine (CTTM) Lab, Department of Surgical Sciences, Uppsala University , 752 37 Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karl-Henrik Grinnemo b Cardio-Thoracic Translational Medicine (CTTM) Lab, Department of Surgical Sciences, Uppsala University , 752 37 Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Woywod a Mimi-Q GmbH , Potsdam Science Park 1, Am Mühlenberg 11, 14476 Potsdam, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jasmin Farouq a Mimi-Q GmbH , Potsdam Science Park 1, Am Mühlenberg 11, 14476 Potsdam, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The development of in vitro assays that can predict clinical outcomes is highly desirable for drug development and personalized medicine. However, conventional in vitro methods often fail to replicate physiological drug pharmacokinetics, posing a challenge to their clinical translation. To address this issue, we adjusted incubation times and concentrations of standard-of-care drugs in the in vitro chemosensitivity assay to reflect those encountered by colorectal cancer patients. Then, for first time, we mimicked the relevant drug exposure of mFOLFOX-6, CapOx and FOLFIRI protocols to predict clinical outcomes. Our pharmacokinetic-based testing on primary colorectal cancer cells accurately predicted responders and non-responders among a cohort of patients (N=6). Classical testing methods such as IC 50 and GI 50 did not reveal any clinically meaningful results. Furthermore, we demonstrated that even subtle changes in drug incubation times could lead to significant variations in the classification of cells as sensitive and resistant, which is not related to mechanisms of action according to categorical clustering. Finally, our pharmacokinetic-based test results were consistent with the historical clinical data on similarities of mFOLFOX-6 and CapOx schemes. Our results contribute to the growing body of evidence that pharmacokinetic-based in vitro testing could bridge the gap between laboratory research and clinical practice. Integration of pharmacokinetic dynamics into in vitro tests could have a significant potential in enhancing drug development and refining personalized treatment strategies. Introduction The development of an in vitro assay to predict clinical outcomes is one of the main research directions in the fields of drug development and personalized medicine. In the early stages of drug development, drug candidates’ efficacy and toxicity are tested in standardized cellular assays, enabling higher throughput. IC 50 and EC 50 ( 1 ), which are derived from dose-response curves in these assays, are essential metrics to study the structure-activity relationship of tested compounds and identify molecular characteristics correlated with drug sensitivity ( 2 – 4 ). With the development of patient-derived organoids technology ( 5 ), IC 50 values were also used as a biomarker to predict response to treatment in patients ( 6 ). However, as recently analyzed in ( 7 ), developing individualised tumor response tests using patient-derived organoids (PDOs) remains a challenging task. The clinical validity of PDO-based tests can vary depending on the characteristics of the tumor and the treatments being tested. Despite promising results in colorectal and pancreatic cancer, data from patients with gastric and esophageal cancer do not convincingly show an association between PDO drug test results and clinical response. Furthermore, the results are predictive for patients who receive treatments based on irinotecan and gemcitabine, while there are conflicting results for oxaliplatin-based treatments ( 8 – 11 ). We hypothesized that these limitations and inconsistencies can be attributed to the differences in response metrics and testing conditions used in the studies. Although IC 50 is the most widely used metric to analyze dose-response curves, it is not the most reliable of the metrics to quantify drug sensitivity ( 12 ). Better performance was shown for the fitted or trapezoidal area under the dose-response curve (AUC), and this parameter was used in ca. 30% of the studies mentioned in the review by Wensink et al. Wensink et al. ( 7 ). An alternative drug response metric, that is insensitive to division number, is growth rate inhibition ( 13 ). Since IC 50 values of cell cycle-dependent cancer drugs, including DNA synthesis inhibitors (i.e. 5-fluorouracil, gemcitabine, platinum-containing drugs) and microtubule inhibitors (i.e. taxanes, vinblastine) are highly sensitive to the number of divisions that occur over the course of a response assay, GR metrics were shown to be superior to assess the effects of these compounds on dividing cells ( 14 ). Other metrics used were decrease in viability, organoid size, and metabolic score ( 7 ). Tan et al. Tan et al. ( 15 ) presented a pan-matrix viability score that yielded promising results in colorectal cancer for combination therapy. With a plethora of metrics mentioned above, the optimal one still needs to be defined. Another dimension of optimization that is needed to achieve clinically relevant results is the experimental setup. Conventional testing approaches poorly replicate the physiological pharmacokinetics of a drug seen in vivo that hamper the translation of in vitro parameters into clinical estimations. One of the aspects is the exposure time. The absence of guidelines for selecting this parameter can lead to high variability in the in vitro data obtained ( 16 ). Among the parameters that should be considered when estimating assay time are the cell doubling time and stability of a drug in solution ( 17 ). The assay time should be long enough to allow tracking the difference in cell viability between control and experimental samples that increases with a number of cell divisions that occurred starting from the zero time point. The example of oxaliplatin clearly illustrates the importance of measuring the stability of compounds in the culture medium used. As demonstrated by Jerremalm et al. Jerremalm et al. ( 18 ), oxaliplatin undergoes rapid degradation in solutions containing chloride ions such as plasma and culture medium. Therefore, a long incubation time could lead to skewed results ( 9 – 11 ). On the other hand, the exposure time of drugs varies widely in patients and is strongly influenced by half-life time ( 19 ) with median values of 0.48 days for small molecules and 4.82 days for biologicals ( Figure 1 ). In organoids the most common incubation time was 6 days regardless of individual characteristics of the drugs that led to overtreatment of the cells with small molecules ( Figure 2 ). Although a longer incubation time results in a shift of a dose-response curve to the lower concentration region, the degree of such shift should be determined in each particular case ( 20 ). Download figure Open in new tab Fig. 1. Distribution chart of half-life times for cancer drugs ( 19 ). Download figure Open in new tab Fig. 2. Assay parameters and outcomes in PDO studies reported ( 8 – 11 , 21 , 22 , 38 – 46 ). The second aspect of the experimental setup is concentration. In dose response assays cells are often exposed to drug concentrations that exceed clinically achievable levels. Only five out of 18 studies reviewed referenced maximum plasma concentrations (C max ) values ( 9 , 10 , 15 , 21 , 22 ). At the same time, the clinical validity of the IC 50 and EC 50 values that exceed C max without any further normalization is questionable ( 23 , 24 ). In oncology, most treatment schemes include drug combinations that are administered according to a certain schedule ( 25 ). Although in published PDO studies the drugs are usually incubated together, if the drugs in a combination exhibit synergism, antagonism, or additive effect, the overall effect could be schedule dependent ( 26 , 27 ). For example, the effects of the ternary combination of irinotecan, 5-fluorouracil, and oxaliplatin, which is a standard of care treatment for colorectal cancer, could be antagonistic or synergistic in the different schedules tested ( 28 ). Therefore, recapitulation of the drug administration sequence is another aspect of the experimental setup that is worth considering. In general, current in vitro methods represent a static ‘closed’ pharmacodynamic model that requires multidimensional optimization of the parameters to overcome the limitations mentioned above. Another approach to improve clinical validity is to simulate an ‘open’ in vivo system. This requires that receptor occupancy, target turnover, and target-drug interactions dynamics are included in the model ( 29 , 30 ). One of the first steps in this direction could be to mimic the pharmacokinetic (PK) profile of a drug or drug combination according to the treatment scheme. With the availability of the PK data for the marketed drugs, this could bring more physiological relevance to the in vitro experiments. Previous studies have demonstrated that imitation of bolus and continuous exposure not only lead to different response rates but also have an impact on the development of drug resistance ( 31 – 34 ). Finally, incorporation of PK exposures into cellular assays was demonstrated to improve translation in xenograft models ( 35 – 37 ). We aim to develop an in vitro assay that can more accurately predict how cancer patients will respond to standard treatments. In this paper, we are addressing thus the following research objectives: Address limitations of current in vitro methods: We highlight the importance of replicating physiological conditions in in vitro assays by illustrating how the drug exposure time impacts the sensitivity status of cancer cells. Develop dynamic dose protocols: We create in vitro protocols that mimic the pharmacokinetics of standard colorectal cancer treatments (specifically, the mFOLFOX-6, CapOx, and FOLFIRI schemes), aiming to replicate clinical exposure dynamics observed in patients. Compare in vitro results with clinical outcomes: We validate the developed in vitro assays by comparing the results with responses of colorectal cancer patients, thereby underlining the relevance and applicability of our findings. Material and methods Human colorectal cancer (CRC) cells were cultured in a complete RPMI 1640 (COLO 205) or DMEM high glucose (HT-29, HCT 116, SW480 and SW620) cell culture medium (Gibco, USA) supplemented with 10% vol. fetal bovine serum, 1% vol. GlutaMax (Gibco, USA) and 1% vol. antibiotic-antimycotic solution (Gibco, USA). Cells were incubated in a cell culture incubator (37°C, 5% CO 2 ). Subculture was carried out every 2-3 days using TrypLE dissociation reagent (Gibco, USA). Primary colorectal cancer cells were derived previously upon written informed consent of the respective patients ( 47 , 48 ) and cultured in advanced DMEM/F12 (Gibco, United States) supplemented with 1% vol. (Gibco, USA), 1% vol. antibiotic-antimycotic solution (Gibco, USA), 1% vol. HEPES (Gibco, USA), 2% vol. B27 (Gibco, USA), 1.25 mM N-acetylcysteine (Sigma, USA), 10 mM nicotinamide (Sigma, USA), 250 ng/ml R-spondin 1 (PeproTech, USA), 100 ng/ml noggin (PeproTech, USA), 50 ng/ml human EGF (Gibco, USA), 10 nM gastrin I (Sigma, USA), 500 nM A83-01 (STEMCELL Technologies, Canada), 1 µ M SB202190 (Tocris Bioscience, UK), 10 nM prostaglandine E2 (Sigma, USA), and 5 µ M Y-27632 (STEMCELL Technologies, Canada) in a Matrigel GFR basement membrane matrix domes (Corning, USA). Cell culture medium was replaced every 48h. Subculture was carried out every 2 weeks using TrypLE dissociation reagent (Gibco, USA). Cells were counted after trypan blue staining (Gibco, USA) using an EVE automated cell counter (NanoEntec, Korea) according to the manufacturer’s protocol. Drug test HT-29, HCT 116, COLO 205, SW480 and SW620 cells were seeded in 96-well plates (TPP, Switzerland) in 100 ul of complete cell culture medium (5000 per well). Primary cultures were diluted in 10 µ l Matrigel GFR (Corning, USA) and seeded in 96-well plates (TPP, Switzerland) (50 spheroids per well). After solidification of the gel, 100 ul of complete cell culture medium was added to each well. After 24h, cell culture medium was replaced with medium containing oxaliplatin, 5-fluorouracil (5-FU) and SN-38 (Tocris Bioscience, UK) respectively. To determine IC 50 and GI 50 values, cells were exposed to serial dilutions covering physiological concentrations of 5-FU (5-FU C max in patients = 341 µ M ( 49 ); 5-FU range in vitro = 0.26 to 800 µ M), oxaliplatin (oxaliplatin C max in patients = 10 µ M ( 50 ); oxaliplatin range in vitro = 0.19 to 200 µ M) and SN-38 (SN-38 C max in patients = 86 nM ( 51 ); SN-38 range in vitro = 0.32 to 1000 nM). 5-FU and SN-38 were prepared in DMSO, while oxaliplatin stock solution was prepared in water ( 52 ). Treatments were compared with vehicle controls that contained the same amount of DMSO and water. Cells were incubated for 72 hours in a cell culture incubator (37°C, 5% CO 2 ) and the medium was replaced with a complete fresh cell culture medium. To evaluate the sensitivity of cancer cells to CRC treatment schemes, we generated unique PK-based in vitro testing protocols, to which we will henceforth refer as dynamic dose testing protocols, as they mimic the sequence, duration of exposure, and drug concentrations of the treatments. To do so, we referenced the hematology and oncology chemotherapy manual ( 25 ) to identify the schedule and timing of drug administrations. Additionally, we analyzed publicly available data on the pharmacokinetics of the drugs included in relevant treatment schemes. For example, the mFOLFOX-6 dynmaic dose protocol includes a 2-hour incubation with 3.37 µ M of oxaliplatin, a 30-minute incubation with 94.5 µ M of 5-FU, and a 46-hour incubation with 3.4 µ M of 5-FU. Similarly, the CapOx dynmaic dose protocol includes a 2-hour incubation with 5.15 µ M of oxaliplatin, followed by two daily 3-hour incubations with 10 µ M of 5-FU for three days. And finally, FOLFIRI dynmaic dose protocol includes a 1.5-hour incubation with 50.7 nM of SN-38, a 30-minute incubation with a mixture of 94.5 µ M of 5-FU and 50.7 nM of SN-38, a 2.5-hour incubation with a mixture of 3.4 µ M of 5-FU and 50.7 nM of SN-38, an 18.5 hour incubation with a mixture of 3.4 µ M of 5-FU and 21.1 nM of SN-38, and a 26-hour incubation with 3.4 µ M of 5-FU. After the last incubation, the medium was replaced with fresh complete cell culture medium and the cells were incubated in a cell culture incubator (37°C, 5% CO 2 ) for a total of 72 hours from the start of the assay. A detailed description of these protocols is provided in the results section. Cell viability was measured with the CellTiter 96 aqueous solution cell proliferation assay kit (Promega, USA) according to the manufacturer’s instructions ( 40 ). The cell growth rate was calculated as described previously ( 13 ). Data analysis For the analysis of the effect of exposure time on the classification of cells as sensitive or resistant to individual drugs, we used previously published data ( 20 ) available online at: https://brb.nci.nih.gov/ETvsCT/ . The parameters ‘EC 50 _Estimate’, ‘Slope_Estimate’, ‘LowerLimit_ Estimate’ and ‘UpperLimit_Estimate’ were utilized to determine the area under dose-response curves (AUC) for all drugs tested and normalized to the control. We set the median of the normalized AUC values distribution as threshold. Cells were classified as ‘sensitive’ or ‘resistant’ to a particular drug under different incubation times based on whether their normalized AUC values were above or below this cutoff. To assess the overall impact of exposure time on cell classification, we normalized the number of cells that retained their sensitivity or resistance to a drug to the total number of cell-drug pairs tested across all incubation times. The calculations were performed using R language ( 53 ) in the RStudio environment ( 54 ) and dplyr ( 55 ), tidyr ( 56 ), stringr ( 55 ) packages. The graphs were plotted in GraphPad Prism software (GraphPad Prism Software, USA) and ggplot2 ( 57 ), ggsci ( 58 ), ggh4x ( 59 ), patchwork ( 60 ) R packages. For the categorical clustering we used the Python module KModes v0.12.2 ( 61 , 62 ). Essentially, the algorithm is initialized with k random modes, representing the clusters characteristic cell response. Then the drugs are assigned to the cluster with the corresponding mode which shares the most amount of same cell responses. Each mode is then updated to the most frequent cell response of all drugs in the corresponding cluster. These two steps of assigning and updating are then repeated until convergence is reached, i.e. the mode of each cluster represents the most frequent cell response of all drugs in that cluster. We considered as final the converged clustering result with the least amount of dissimilarities between modes and corresponding drugs out of 1000 random initializations. Results and discussion Exposure time - sensitivity status relationship Several factors have an impact on the response of cells to drugs, and the duration of exposure is critical. In the Evans et al. Evans et al. ( 20 ) study 320 compounds were screened in the NCI60 cell line panel using 2, 3, 7, and 11 days exposure times respectively. Although a positive linear relationship was observed between IC 50 values and exposure, it was not clear whether the individual sensitivity status of the cells remains similar at different incubation times. Understanding this possible dependency of incubation time and drug sensitivity could play an important role in selection of assay parameters and further clinical decision making, e.g. personalized treatment recommendations after PDO testing. First, we calculated normalized to control AUC values for the 16225 concentration-response curves present in the dataset. Then we set the median of the normalized AUC values distribution as a threshold for each drug. Cells were classified as ‘sensitive’ or ‘resistant’ to a particular drug in a 2-day incubation assay. Surprisingly, we observed that even small changes in incubation time (3 days vs. 2 days) led to significant changes in the order of cell line sensitivities ( Figure 3A ). As clearly seen in the 5-FU example, the cells that were labeled as ‘sensitive’ in a 2-day incubation assay were distributed across all ‘Normalized AUC’ axis range in a 3-day incubation assay without any clear new cutoff value. Similar observations were found for all drugs (data not shown). Download figure Open in new tab Fig. 3. (A) Distribution of ‘sensitive’ and ‘resistant’ cell lines to 5-FU based on 2-day incubation data after 3, 7, and 11 days of incubation. (B) Normalized number of cell lines that maintain their 2-day incubation sensitivity status after 3, 7, and 11 days of incubation. To assess the overall impact of exposure time on cell classification, we normalized the number of cells that retained their position relative to the median values to the total number of cell drug pairs for each incubation time ( Figure 3B ). The median value of cells that preserved the sensitivity status compared to 2-day incubation after 3 and 7-day incubation was roughly 0.5. More profound changes in the sensitivity status were found for cells incubated for 2 days and 11 days. However, this discrepancy could be attributed to the differences in culture methods, as the data for the 11-day incubation was acquired in spheroid culture, unlike the monolayer cultures for the other incubation times. One of the possible explanations for the change in sensitivity status could be the mechanism of the action of drugs. For compounds that inhibit growth rate or exhibit cytotoxic effect in a cell cycle-dependent manner, the IC 50 or EC 50 values are highly sensitive to the number of divisions that occur during exposure time ( 13 ). Such compounds include selective S-phase selective agents, DNA-damaging agents, DNA methyltransferase inhibitors, DNA polymerase inhibitors and intracellular serine-threonine kinase inhibitors. On the other hand, histone deacetylase inhibitors, receptor tyrosine kinase inhibitors, EZH2 inhibitors, gamma secretase inhibitors, and SMO inhibitors had no dependence on exposure time in manifesting growth inhibition or were not effective on cell viability even after 11-day exposure ( 20 ). To assess whether the mechanisms of action affect the preservation of the sensitivity state and to identify the ‘exposure-agnostic’ drugs, we compared the number of cells with consistent sensitivity states between different drug types ( Figure 4 , Supplementary data file 1). Download figure Open in new tab Fig. 4. Normalized number of cell lines that had a consistent sensitivity status at all incubation times with a drug. Although the range of cells with stable sensitivity varies between 10% and 55% for different drugs, an obvious explanation for the sensitivity status of the cells could not be found based on the mechanism of action of the drugs. Therefore, we used k -mode clustering in attempt to derive such drug clusters. The cell lines were labeled according to the their response status to a drug across different incubation times (‘sensitive’, ‘resistant’, ‘unstable’). As k -mode clustering relies on a predefined number of clusters, we tried from k = 4 to 11, representing the number of mechanisms of action into which the tested drugs can be grouped (Supplementary data file 2). To interpret the results, we carried out a k -mode analysis and compared the derived clusters with the categorisation according to the known mechanism of action of drugs as visualised in figure 5 for k = 4. Download figure Open in new tab Fig. 5. Mode Analysis of k -mode clustering of cell responses (left) and comparison with know mechanism of action of the drugs (right). The modes of each cluster are indicated by the up or down facing grey bars for sensitive and resistant cell responses (missing for unstable cells). The yellow and blue circles indicate the relative frequency of sensitive and resistant behaviour of each cell lines. The only insight that might be drawn is that drugs in cluster 3 are almost all protein kinase inhibitors. However, the the drugs with such mechanism of action were also present in other clusters. In any case, the results did not allow to identify any ‘exposure-agnostic’ category of drugs. In addition to the factors mentioned above, drug stability in the cell culture medium may also account for these results. As shown in the example of platinum complexes, the presence of DMSO and chlorine ions may have an impact on actual concentrations of drugs in the assay ( 18 , 52 ). Furthermore, the presence of DMSO and serum can affect the response to compounds with limited aqueous solubility ( 30 ). Therefore, we hypothesized that considering individual drug characteristics and pharmacokinetics in in vitro tests, i.e. using the dynamic dose approach, could improve the clinical validity of the corresponding data. To validate this hypothesis, we generated dynamic dose testing protocols for CRC treatment schemes and assessed the viability of CRC cell lines and primary colorectal cancer cell cultures in static and dynamic exposure assays. Generation of dynamic dose protocols for the FOLFOX-6, CapOx and FOLFIRI schemes To develop dynamic dose protocols, we referred to the clinical recommendations ( 25 ). The main chemotherapy treatment options for the CRC patient are platinum-containing (FOLFOX and CapOx) or irinotecan-based (FOLFIRI) schemes. The sequences and doses of the drug constituting these schemes are presented in Table 1 . We analysed the available PK data to determine the relevant in vitro concentrations individually for each scheme. View this table: View inline View popup Download powerpoint Table 1. Clinical schedules for the FOLFOX-6, CapOx and FOLFIRI schemes Dynamic dose mFOLFOX-6 protocol Oxaliplatin infusion Most pharmacokinetic studies on oxaliplatin primarily report data on platinum content rather than the drug itself, with measurements often taken in whole plasma, ultrafiltrate and erythrocytes ( 50 , 63 ). However, it is important to note that oxaliplatin undergoes spontaneous metabolism, resulting in the platinum concentration not accurately reflecting the concentration of oxaliplatin. Additionally, the metabolites of oxaliplatin are either inactive or present at significantly lower concentrations than the parent compound, emphasizing the significance of considering the oxaliplatin content when determining in vitro concentrations ( 64 ). Studies conducted by Ehrsson et al. Ehrsson et al. ( 65 ), and Ehrsson and Wallin Ehrsson and Wallin ( 66 ), employed derivatization to measure oxaliplatin concentration, revealing an average maximum concentration of 1.44 ± 0.20 µ g/ml at a dose of 85 mg/m 2 , with the area under the PK curve (AUC PK ) of 161 ± 23 µ g · min/mL. Consequently, for in vitro testing, a concentration of 3.37 µ M and an incubation time of 2 hours were selected. Notably, the incubation was performed in DPBS with 10 mM HEPES, as prior research demonstrated that after 2 hours in DPBS, around 80% of oxaliplatin remains in an intact state ( 18 ). 5-FU bolus The pharmacokinetics of 5-fluorouracil (5-FU) following bolus administration in doses of 300-600 mg/m 2 have been extensively studied, showing peak concentrations in the millimolar range and rapid subsequent decline, with AUC PK values ranging from 71 to 125 µ M · h ( 49 , 67 – 69 ). For our in vitro experiment, we selected the AUC PK data for the first 30 minutes after administration, as more than half of the AUC PK values fall within this range (Collins et al., 1980). In this case, the AUC PK has been identified to be 47.25 µ M · h ( 68 ). Thus, we chose a concentration of 94.5 µ M and an incubation time of 30 minutes to ensure a short exposure of high-concentration 5-FU to the cells. 5-FU infusion The in vitro concentration for simulating 5-FU infusion was determined based on the available data on mean 5-FU concentration and AUC PK . A mean AUC PK of 20.35 µ M · h was identified for a 46-hour infusion, resulting in an in vitro concentration of 3.4 µ M and an incubation time of 46 hours ( 70 ). These findings align with 5-FU plasma concentration measurements ( 71 ). Dynamic dose CapOx protocol Oxaliplatin infusion Considering a linear increase in C max , the C max for a dose of 130 mg/m 2 is 2.2 µ g/ml (397.294 g/mol, 5.5 µ M). Additionally, the AUC PK was determined to be 161 ± 23 µ g·min/mL for a dose of 85 mg/m 2 , which, assuming linearity, corresponds to 246 µ g·min/mL = 4.1 µ g·h/mL = 10.3 µ M·h. Therefore, for in vitro testing, a concentration of 5.15 µ M and an incubation time of 2 hours were selected. The incubation was conducted in DPBS with 10 mM HEPES, as it has been previously recommended for the mFOLFOX-6 protocol. Capecitabine Capecitabine (5’-deoxy-5-fluorouridine, 5’-DFUR) is a prodrug that undergoes conversion to 5-fluorouracil primarily in the liver. Subsequent metabolism leads to the formation of 5-fluoro-2’-deoxyuridine monophosphate (FdUMP) and 5-fluorouridine triphosphate (FUTP) in both normal and tumor cells. After a single dose of capecitabine at 1250 mg/m 2 , the AUC PK for 5-FU reaches 4.4 µ M · h over a 6-hour period ( 72 , 73 ). Approximately 90% of the AUC PK is achieved within 3 hours. In the CapOx protocol, capecitabine is administered at a dose of 1000 mg/m 2 , resulting in a concentration of 1.2 µ M after a 3-hour exposure. However, when capecitabine is administered, 5-FU tends to accumulate in tissues. In the case of liver metastases, the tissue concentration of 5-fluorouracil can be nearly 10 times higher than the plasma concentration ( 73 ). Therefore, to simulate a single dose of capecitabine in vitro , a concentration of 10 µ M and an incubation time of 3 hours were selected. Considering the dosing schedule, this incubation was performed twice daily. Dynamic dose FOLFIRI protocol Irinotecan infusion Irinotecan is classified as a prodrug that undergoes liver metabolism to its active form, SN-38. Therefore, in our in vitro tests, we included SN-38. Based on a single-dose infusion of irinotecan at 180 mg/m 2 , the half-life of SN-38 was 11.02 hours ( 51 ). The calculated area under the PK curve was 269 ng · h/mL for SN-38. Considering the relatively long half-lives, we decided to simulate high and low concentration exposures through two separate incubations: 5.5 hours with 50.7 nM (SN-38), and 18.5 hours with 21.1 nM (SN-38). Furthermore, considering the subsequent administrations of 5-FU during treatment, we prepared double mixtures of two drugs to mimic their simultaneous presence in the bloodstream of patients. 5-FU bolus 5-FU concentration of 94.5 µ M and an incubation time of 30 minutes as in the in vitro mFOLFOX-6 protocol. 5-FU infusion 5-FU concentration of 3.4 µ M and an incubation time of 46 hours as in the in vitro mFOLFOX-6 protocol. The resulting mFOLFOX-6, CapOx and FOLFIRI dynamic dose protocols are presented in Table 2 . These protocols along with standard dose-response assays were used further to assess the sensitivity of colorectal cancer cells. View this table: View inline View popup Download powerpoint Table 2. Dynamic dose protocols for the FOLFOX-6, CapOx and FOLFIRI schemes Sensitivity testing of colorectal cancer cells The dynamic dose test better predicts the response to chemotherapy in CRC patients At first, cells were exposed to a series of constant concentrations of the same drugs for 72 hours. The patients in this cohort (N=6) received several chemotherapy lines, including mFOLFOX-6, CapOx, FOLFIRI or Capecitabin mono schemes (more detailed information is provided in Table S1 ). Four patients did not respond to any treatment, while two responded to 5-FU–irinotecan combination therapy. The response values had a wide distribution with the 5-FU IC 50 range = 30.25 to 984.37 µ M, oxaliplatin IC 50 range = 29.88 to 294.66 µ M and SN-38 IC 50 range = 33.55 to 103.99 nM. Surprisingly, the cells derived from the responder had 5-FU values on opposite sides of the spectrum (30.25 and 313.38 µ M). SN-38 IC 50 values were on the lower end which is consistent with previous studies ( 10 ). However, it was not possible to determine a cutoff without misclassifying responders and non-responders. Therefore, the IC 50 metrics did not prove to be effective for predicting the response to treatment in our study ( Figure 6 ). Download figure Open in new tab Fig. 6. Fitted dose–response curves of the primary colorectal cancer cells exposed to the 5-FU (A), oxaliplatin (B) and SN-38 (C). Similar findings were observed for the GI 50 metrics ( Figure 7 ). It was also not possible to identify quadrants in 2D of 5-FU - Oxaliplatin GI 50 values ( Figure 7D ) and 5-FU - SN-38 GI 50 values ( Figure 7E ) for the accurate stratification of cells based on the clinical response. In general, these results align with previous studies that suggest that the SN-38 IC 50 or GI 50 values rather than 5-FU are better biomarkers for predicting responses to the FOLFIRI scheme. Interestingly, we observed a stronger correlation between the IC 50 and GI 50 values for 5-FU and SN-38 than for oxaliplatin ( Figure S1 ). Because the patients were treated with combination chemotherapy, we performed additional experiments with mixtures of the applied drugs. Cells were exposed to clinically reachable concentrations of 5-FU and oxaliplatin (3.4 µ M and 3.37 µ M) as well as 5-FU and SN-38 (3.4 µ M and 20.7 nM) combinations, or 5-FU alone (3.4 µ M) for 46 hours. The combination of 5-FU and oxaliplatin was used to assess response to mFOLFOX-6 or CapOx schemes, while 5-FU – SN-38 and 5-FU alone to evaluate responses to FOLFIRI or capecitabine monotherapy, respectively. The test results with clinically relevant concentrations made it possible to clearly identify non-responders to oxaliplatin-containing therapy schemes and capecitabine monotherapy ( Figure 8A ). These results were also more consistent with the clinical outcomes for the FOLFIRI scheme. Recently another study by Tan et al. Tan et al. ( 15 ) demonstrated a 83% accuracy in predicting responses in CRC patients supporting the importance of utilization of clinically relevant concentrations of chemotherapeutic drugs in in vitro tests. However, we were still unable to establish a cutoff value for the classification of responders and non-responders to irinotecan treatment using the IC 50 and GI 50 metrics. From a clinical utility perspective, the clear identification of resistance holds the same, if not higher, value as the prediction of sensitivity, because ineffective treatment cycles worsen a patient’s condition and limit further treatment options. Download figure Open in new tab Fig. 7. GI 50 values of the primary colorectal cancer cells exposed to 5-FU (A), oxaliplatin (B), SN-38 (C) and 2D plots for the drugs associated with oxaliplatin (D) or irinotecan-containing schemes (E). Download figure Open in new tab Fig. 8. The in vitro response of primary colorectal cancer cells exposed to drug mixtures at clinically reachable concentrations (A), treated according to PK-based protocols (B), and a general comparison of responses using different methods (C). Finally, to evaluate the performance of our dynamic dose test, we treated the cells according to the corresponding protocols for the FOLFOX-6, CapOx, and FOLFIRI schemes. As can be seen in ( Figure 8B ), shorter exposure times, recapitulation of the sequence and clinically relevant concentrations resulted in a more widely dispersed response distribution to the FOLFIRI treatment. Such an approach enabled stratification of the cells for all chemotherapy schemes tested using the cutoff of 15%. Therefore, the dynamic dose test demonstrated the best performance for classifying ‘responders’ and ‘non-responders’ in a small cohort of primary colorectal cancer cells among all the methods used ( Figure 8C ). The dynamic dose test recapitulates the equivalence of the mFOLFOX-6 and CapOx Another interesting result we observed during the evaluation of the response to mFOLFOX-6 and CapOx schemes on the primary colorectal cancer cells derived from patient 2. According to the generated dynamic dose mFOLFOX-6 and CapOx protocols ( Table 2 ), cells were exposed to different concentrations of oxaliplatin (3.37 µ M for mFOLFOX-6 and 5.15 µ M for CapOx) and 5-FU (94.5 µ M followed by 3.4 µ M for mFOLFOX-6 and 10 µ M for CapOx) for different incubation times. This led to differences in AUC PK for oxaliplatin (6.74 µ M·h for mFOLFOX-6 and 10.30 µ M·h for CapOx) and 5-FU (203.65 µ M·h for mFOLFOX-6 and 180.00 µ M h for CapOx). Despite the differences in testing conditions, the response was similar for both schemes. Clinically, these two schemes have been demonstrated to exhibit no statistical differences in overall survival and overall response rate ( 74 , 75 ). Therefore, we quantified the response of both primary colorectal cancer cells ( Figure 9A ) and CRC cell lines ( Figure 9B ) to evaluate whether the PK-based dynamic dose test could recapitulate these findings in vitro . Download figure Open in new tab Fig. 9. The in vitro response of primary colorectal cancer cells (A) and CRC cell lines (B) to mFOLFOX-6 and CapOx treatment. As can be seen in ( Figure 9 ), dynamic dose test results were consistent with the historical clinical data. Dynamic dose tests revealed a similar response of all colorectal cancer cells to mFOLFOX-6 and CapOx. This highlights the potential utility of this test for comparing the efficacy of treatment schemes during drug development. Conclusions In 2021, the Oncology Center of Excellence has launched a Project Optimus initiative to reform the paradigm of dose optimization and dose selection in oncology drug development ( 76 ). The later published guidelines highlighted that the currently used clinical and in vitro approaches for dose finding (e.g. maximum tolerated dose) are not optimal and may not apply to the latest anticancer drugs, such as targeted therapies ( 77 ). Conventional drug testing methods under the static conditions that are used in early drug development may hamper the discovery of optimal drug doses. This initial non-optimal study design choice can lead to the need for further dose optimization studies in the late stages of clinical development resulting in a high failure rate and excessive costs. For example, sotorasib ( 78 ), capecitabine ( 79 ) and gemtuzumab ( 80 ) required post-approval dose adjustments to improve safety and tolerability. There are multiple dimensions for improving preclinical development, including more physiological disease modeling using organs-on-a-chip, development of more sophisticated cell models such as PDO and the utilization of computational multi-omics approaches. In our study, we have assessed the potential of pharmacokinetic-based in vitro testing to improve the drug sensitivity testing of colorectal cancer cells. The key results and their implications are outlined as follows: Superior classification of ‘responders’ vs. ‘non-responders’: Our PK-based dynamic dose test demonstrated the best performance for classifying ‘responders’ and ‘non-responders’ in a small cohort of primary colorectal cancer cells among all the methods used. The best result among classical approaches was achieved in a test with exposure to clinically reachable concentrations (only one misclassified case). IC 50 and GI 50 metrics were not univocal for different drugs and were particularly inaccurate for 5-FU. Alignment with historical clinical data: Dynamic dose test results were also consistent with the existing clinical data on similarities of mFOLFOX6 and CapOx schemes. Application in drug development: In addition to measuring individual patient responses, this approach also has the potential for ‘pharmacological calibration’ of a drug candidate before entering a phase I clinical trial ( 81 ). It includes optimising the dose and schedule to balance efficacy and toxicity, which current approaches are reported to underestimate ( 82 ). Improvement of PK/PD modeling: The dose-response function in PK/PD modeling is typically described by the Hill equation ( 83 ), which does not account for in vitro exposure time and may vary depending on the experimental setup. Integrating pharmacokinetics into in vitro tests enables direct measurement of dose-dependent drug effects, facilitating high-throughput data acquisition necessary for PK/PD modeling and optimizing schedules in the frequency domain ( 84 ). Impact on personalized treatment: One of the advantages of the dynamic dose test is its flexibility in adjusting the test procedure based on the treatment schedule and the individual PK characteristics of a drug, a patient, or a group of patients. In sum, this research contributes to the ongoing effort to bridge the gap between in vitro assays and clinical outcomes. Although these assumptions have yet to be validated, the results presented in this study, along with findings from other groups, suggest that the PK-based dynamic dose testing approach may have considerable potential in fields of drug development and personalized treatment. Data availability All additional data that supports the findings of this study is available on request from the corresponding author. Author contributions A.A.P., B.R.B. - conceptualization, supervision; A.A.P., B.R.B., S.N. - data curation, formal analysis, investigation, methodology; A.A.P., B.R.B., S.R., K.-H.G., M.W., J.F. - original draft, review and editing. Competing interests A.A.P., B.R.B., M.W. and J.F. own shares in the Mimi-Q GmbH. A.A.P. and S.N. are co-inventors of the patent describing the testing approach used. All other authors declare no conflict of interest. Supplementary material View this table: View inline View popup Download powerpoint Table S1. Treatment history of the patients Download figure Open in new tab Fig. S1. Correlation between IC 50 and GI 50 for the drugs tested. ACKNOWLEDGEMENTS We thank Prof. Udo Schumacher, Dr. Jens Hoffmann and Dr. Martin Schumacher for their valuable recommendations regarding methodology and manuscript structure. 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