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A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

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The discovery of brain therapeutics faces a significant challenge due to the low translatability of preclinical results into clinical success. To address this gap, several efforts have been made to obtain more translatable neuronal models for phenotypic screening. These models allow the selection of active compounds without predetermined knowledge of drug targets. In this review, we present an overview of various existing models within the field, examining their strengths and limitations, particularly in the context of neuropathic pain research. We illustrate the usefulness of these models through a comparative review in three crucial areas: i) the development of novel phenotypic screening strategies specifically for neuropathic pain, ii) the validation of the models for both primary and secondary screening assays, and iii) the use of the models in target deconvolution processes.

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Chameleonicity (the capacity of a molecule to adapt its conformations to the environment) may help to identify orally bioavailable drugs in the beyond-Rule-of-5 chemical space. Computational methods to predict the chameleonic behaviour of degraders have not yet been reported and the identification of molecular chameleons still relies on experimental evidence. Therefore, there is a need to tune predictions with experimental data. Here, we employ PROTAC-1 (a passively cell-permeable degrader), for which NMR and physicochemical data prove the chameleonic behaviour, to benchmark the capacity of two conformational sampling algorithms and selection schemes. To characterize the conformational ensembles in both polar and nonpolar environments, we compute three molecular properties proven to be essential for cell permeability: conformer shape (radius of gyration), polarity (3D PSA), and the number of intramolecular hydrogen bonds. Energetic criteria were also considered. Infographics monitored the simultaneous variation of those properties in computed and NMR conformers. Overall, we provide key points for tuning conformational sampling tools to reproduce PROTAC-1 chameleonicity according to NMR evidence. This study is expected to improve the design of PROTAC drugs and the development of computational sustainable strategies to exploit the potential of new modalities in drug discovery.

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High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFRT790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations.

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BACKGROUND: "Branched tail" oxyquinolines, and adaptaquin in particular, are potent HIF prolyl hydroxylase inhibitors showing promising results in in vivo hemorrhagic stroke models. The further improvement of the potency resulted in identification of a number of adaptaquin analogs. Early evaluation of toxicity and metabolism is desired right at the step of lead selection. OBJECTIVE: The aim of the study is to characterize the toxicity and metabolism of adaptaquin and its new improved analogs. METHOD: Liver-on-a-chip technology with differentiated HepaRG cells followed by LC-MS detection of the studied compounds and metabolites of the P450 substrate-inhibitor panel for CYP2B6, CYP2C9, CYP2C19, and CYP3A4. RESULTS: The optimized adaptaquin analogs show no toxicity up to a 100-fold increased range over EC50. The drugs are metabolized by CYP3A4 and CYP2B6 as shown with the use of the cytochrome P450 substrate-inhibitor panel designed and optimized for preclinical evaluation of drugs' in vitro biotransformation on a 3D human histotypical cell model using "liver-on-a-chip" technology. Activation of CYP2B6 with the drugs tested has been observed. A scheme for adaptaquin oxidative conversion is proposed. CONCLUSION: The optimized adaptaquin analogs are suitable for further preclinical trials. Activation of CYP2B6 with adaptaquin and its variants points to a potential increase in Tylenol toxicity if administered together.

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We have developed a high content 384-well, image-based assay to estimate the effect of compound treatment on Trypanosoma cruzi amastigotes in 3T3 fibroblasts. In the same well, the effect of compound activity on host cells can also be determined, as an initial indicator of cytotoxicity. This assay has been used to identify active compounds from an in-house library of compounds with either known biological activity or that are FDA approved, and separately, from the Medicines for Malaria Venture Malaria Box collection. Active compounds were screened against T. cruzi trypomastigotes, utilising an assay developed with the viability dye resazurin. Twelve compounds with reconfirmed solid sample activity, with IC50 values of less than 10 µM and selectivity indices to T. cruzi amastigotes over 3T3 host cells of between >22 and 319 times were identified from these libraries. As 3T3 cells are contact inhibited, with limited proliferation in the assay, selective compounds of interest were profiled in a separate assay to estimate the viability of compound treated, replicating HEK293 cells. Selective compounds that were not previously reported in the literature were further profiled by extending the incubation time against amastigote infected 3T3 cells to determine if there were residual amastigotes post-treatment, important for the consideration of the exposure time required for further biological characterisation. The assay development process and the suitability of identified compounds as hit molecules for Chagas disease research are discussed.

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