Partnering with Big Pharma-What Academics Need to Know.

Knowledge of the parameters of drug development can greatly aid academic scientists hoping to partner with pharmaceutical companies. Here, we discuss the three major pillars of drug development-pharmacodynamics, pharmacokinetics, and toxicity studies-which, in addition to pre-clinical efficacy, are critical for partnering with Big Pharma to produce novel therapeutics.

Phosphorus-mediated sp2-sp3 couplings for C-H fluoroalkylation of azines.

Fluoroalkyl groups profoundly affect the physical properties of pharmaceuticals and influence almost all metrics associated with their pharmacokinetic and pharmacodynamic profile1-4. Drug candidates increasingly contain trifluoromethyl (CF3) and difluoromethyl (CF2H) groups, and the same trend in agrochemical development shows that the effect of fluoroalkylation translates across human, insect and plant life5,6. New fluoroalkylation reactions have undoubtedly stimulated this shift; however, methods that directly convert C-H bonds into C-CF2X groups (where X is F or H) in complex drug-like molecules are rare7-13. Pyridines are the most common aromatic heterocycles in pharmaceuticals14, but only one approach-via fluoroalkyl radicals-is viable for achieving pyridyl C-H fluoroalkylation in the elaborate structures encountered during drug development15-17. Here we develop a set of bench-stable fluoroalkylphosphines that directly convert the C-H bonds in pyridine building blocks, drug-like fragments and pharmaceuticals into fluoroalkyl derivatives. No preinstalled functional groups or directing groups are required. The reaction tolerates a variety of sterically and electronically distinct pyridines, and is exclusively selective for the 4-position in most cases. The reaction proceeds through initial formation of phosphonium salts followed by sp2-sp3 coupling of phosphorus ligands-an underdeveloped manifold for forming C-C bonds.

Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.

Target-mediated drug disposition (TMDD) is a phenomenon characterized by a drug's high-affinity binding to a target molecule, which significantly influences its pharmacokinetic profile within an organism. The comprehensive TMDD model delineates this interaction, yet it may become overly complex and computationally demanding in the absence of specific concentration data for the target or its complexes. Consequently, simplified TMDD models employing quasi-steady state approximations (QSSAs) have been introduced; however, the precise conditions under which these models yield accurate results require further elucidation. Here, we establish the validity of three simplified TMDD models: the Michaelis-Menten model reduced with the standard QSSA (mTMDD), the QSS model reduced with the total QSSA (qTMDD), and a first-order approximation of the total QSSA (pTMDD). Specifically, we find that mTMDD is applicable only when initial drug concentrations substantially exceed total target concentrations, while qTMDD can be used for all drug concentrations. Notably, pTMDD offers a simpler and faster alternative to qTMDD, with broader applicability than mTMDD. These findings are confirmed with antibody-drug conjugate real-world data. Our findings provide a framework for selecting appropriate simplified TMDD models while ensuring accuracy, potentially enhancing drug development and facilitating safer, more personalized treatments.

Effect of food and polymorphisms in SLCO2B1, CYP3A4 and UGT1A4 on pharmacokinetics of abiraterone and its metabolites in Chinese volunteers.

AIMS: Abiraterone acetate, a prodrug of abiraterone (ABI), provides an efficient therapeutic option for metastatic castration-resistant prostate cancer patients. ABI undergoes extensive metabolism in vivo and is transformed into active metabolites Δ4 -abiraterone and 3-keto-5α-abiraterone as well as inactive metabolites abiraterone sulfate and abiraterone N-oxide sulfate. We aimed to examine the effect of polymorphisms in SLCO2B1, CYP3A4 and UGT1A4 on the pharmacokinetics of ABI and its metabolites. METHODS: In this study, 81 healthy Chinese subjects were enrolled and divided into 2 groups for fasted (n = 45) and fed (n = 36) studies. Plasma samples were collected after administering a 250 mg abiraterone acetate tablet followed by liquid chromatography-tandem mass spectrometry analysis. Genotyping was performed on a MassARRAY system. The association between SLCO2B1, CYP3A4, UGT1A4 genotype and pharmacokinetic parameters of ABI and its metabolites was assessed. RESULTS: Food effect study demonstrated high fat meal remarkedly increased systemic exposure of ABI and its metabolites. The geometric mean ratio and 90% confidence interval of area under the plasma concentration-time curve from time 0 to the time of the last quantifiable concentration (AUC0-t ) and maximum plasma concentration (Cmax ) of ABI in fed state vs. fasted state were 351.64% (286.86%-431.04%) and 478.45% (390.01%-586.94%), respectively, while the corresponding results were ranging from 145.11% to 269.42% and 150.10% to 478.45% for AUC0-t and Cmax of ABI metabolites in fed state vs. fasted state, respectively. The SLCO2B1 rs1077858 had a significant influence on AUC0-t and Cmax , while 7 other SLCO2B1 variants prolonged half-life of ABI under both fasted and fed conditions. As for ABI metabolites, the systemic exposure of Δ4 -abiraterone, abiraterone sulfate and abiraterone N-oxide sulfate as well as the elimination of 3-keto-5α-abiraterone were significantly affected by SLCO2B1 polymorphisms. Polymorphisms in CYP3A4 and UGT1A4 did not significantly affect pharmacokinetics of ABI and its metabolites. CONCLUSION: Polymorphisms in SLCO2B1 were significantly related to the pharmacokinetic variability of ABI and its metabolites under both fasted and fed conditions.

Relative Performance of Volume of Distribution Prediction Methods for Lipophilic Drugs with Uncertainty in LogP Value.

PURPOSE: The goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VDss) predictions, including intermediate fut and Kp values, from six methods: Oie-Tozer, Rodgers-Rowland (tissue-specific Kp and only muscle Kp), GastroPlus, Korzekwa-Nagar, and TCM-New. METHOD: A sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VDss was also calculated for the specific literature logP values. Error prediction analysis was conducted by analyzing prediction errors by source of logP values, drug, and overall values. RESULTS: The Rodgers-Rowland methods were highly sensitive to logP values, followed by GastroPlus and Korzekwa-Nagar. The Oie-Tozer and TCM-New methods were only modestly sensitive to logP. Hence, the relative performance of these methods depended upon the source of logP value. As logP values increased, TCM-New and Oie-Tozer were the most accurate methods. TCM-New was the only method that was accurate regardless of logP value source. Oie-Tozer provided accurate predictions for griseofulvin, posaconazole, and isavuconazole; GastroPlus for itraconazole and isavuconazole; Korzekwa-Nagar for posaconazole; and TCM-New for griseofulvin, posaconazole, and isavuconazole. Both Rodgers-Rowland methods provided inaccurate predictions due to the overprediction of VDss. CONCLUSIONS: TCM-New was the most accurate prediction of human VDss across four drugs and three logP sources, followed by Oie-Tozer. TCM-New showed to be the best method for VDss prediction of highly lipophilic drugs, suggesting BPR as a favorable surrogate for drug partitioning in the tissues, and which avoids the use of fup.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Challenges of pediatric pharmacotherapy: A narrative review of pharmacokinetics, pharmacodynamics, and pharmacogenetics.

PURPOSE: Personalized pharmacotherapy, including for the pediatric population, provides optimal treatment and has emerged as a major trend owing to advanced drug therapeutics and diversified drug selection. However, it is essential to understand the growth and developmental characteristics of this population to provide appropriate drug therapy. In recent years, clinical pharmacogenetics has accumulated knowledge in pediatric pharmacotherapy, and guidelines from professional organizations, such as the Clinical Pharmacogenetics Implementation Consortium, can be consulted to determine the efficacy of specific drugs and the risk of adverse effects. However, the existence of a large knowledge gap hinders the use of these findings in clinical practice. METHODS: We provide a narrative review of the knowledge gaps in pharmacokinetics (PK) and pharmacodynamics (PD) in the pediatric population, focusing on the differences from the perspective of growth and developmental characteristics. In addition, we explored PK/PD in relation to pediatric clinical pharmacogenetics. RESULTS: The lack of direct and indirect biomarkers for more accurate assessment of the effects of drug administration limits the current knowledge of PD. In addition, incorporating pharmacogenetic insights as pivotal covariates is indispensable in this comprehensive synthesis for precision therapy; therefore, we have provided recommendations regarding the current status and challenges of personalized pediatric pharmacotherapy. The integration of clinical pharmacogenetics with the health care system and institution of educational programs for health care providers is necessary for its safe and effective implementation. A comprehensive understanding of the physiological and genetic complexities of the pediatric population will facilitate the development of effective and personalized pharmacotherapeutic strategies.

Discovering clinical drug-drug interactions with known pharmacokinetics mechanisms using spontaneous reporting systems and electronic health records.

OBJECTIVE: Although the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation. METHODS: We analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals. RESULTS: Out of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals. CONCLUSION: The fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs.

Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Low-dimensional neural ODEs and their application in pharmacokinetics.

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Prediction of Human Pharmacokinetics of E0703, a Novel Radioprotective Agent, Using Physiologically Based Pharmacokinetic Modeling and an Interspecies Extrapolation Approach.

E0703, a new steroidal compound optimized from estradiol, significantly increased cell proliferation and the survival rate of KM mice and beagles after ionizing radiation. In this study, we characterize its preclinical pharmacokinetics (PK) and predict its human PK using a physiologically based pharmacokinetic (PBPK) model. The preclinical PK of E0703 was studied in mice and Rhesus monkeys. Asian human clearance (CL) values for E0703 were predicted from various allometric methods. The human PK profiles of E0703 (30 mg) were predicted by the PBPK model in Gastro Plus software 9.8 (SimulationsPlus, Lancaster, CA, USA). Furthermore, tissue distribution and the human PK profiles of different administration dosages and forms were predicted. The 0.002 L/h of CL and 0.005 L of Vss in mice were calculated and optimized from observed PK data. The plasma exposure of E0703 was availably predicted by the CL using the simple allometry (SA) method. The plasma concentration-time profiles of other dosages (20 and 40 mg) and two oral administrations (30 mg) were well-fitted to the observed values. In addition, the PK profile of target organs for E0703 exhibited a higher peak concentration (Cmax) and AUC than plasma. The developed E0703-PBPK model, which is precisely applicable to multiple species, benefits from further clinical development to predict PK in humans.

Determining the Delamanid Pharmacokinetics/Pharmacodynamics Susceptibility Breakpoint Using Monte Carlo Experiments.

Antimicrobial susceptibility testing, based on clinical breakpoints that incorporate pharmacokinetics/pharmacodynamics (PK/PD) and clinical outcomes, is becoming a new standard in guiding individual patient therapy as well as for drug resistance surveillance. However, for most antituberculosis drugs, breakpoints are instead defined by the epidemiological cutoff values of the MIC of phenotypically wild-type strains irrespective of PK/PD or dose. In this study, we determined the PK/PD breakpoint for delamanid by estimating the probability of target attainment for the approved dose administered at 100 mg twice daily using Monte Carlo experiments. We used the PK/PD targets (0- to 24-h area under the concentration-time curve to MIC) identified in a murine chronic tuberculosis model, hollow fiber system model of tuberculosis, early bactericidal activity studies of patients with drug-susceptible tuberculosis, and population pharmacokinetics in patients with tuberculosis. At the MIC of 0.016 mg/L, determined using Middlebrook 7H11 agar, the probability of target attainment was 100% in the 10,000 simulated subjects. The probability of target attainment fell to 25%, 40%, and 68% for PK/PD targets derived from the mouse model, the hollow fiber system model of tuberculosis, and patients, respectively, at the MIC of 0.031 mg/L. This indicates that an MIC of 0.016 mg/L is the delamanid PK/PD breakpoint for delamanid at 100 mg twice daily. Our study demonstrated that it is feasible to use PK/PD approaches to define a breakpoint for an antituberculosis drug.

Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development.

In drug development, preclinical safety and pharmacokinetics assessments of candidate drugs to ensure the safety profile are a must. While in vivo and in vitro tests are traditionally used, experimental determinations have disadvantages, as they are usually time-consuming and costly. In silico predictions of these preclinical endpoints have each been developed in the past decades. However, only a few web-based tools have integrated different models to provide a simple one-step platform to help researchers thoroughly evaluate potential drug candidates. To efficiently achieve this approach, a platform for preclinical evaluation must not only predict key ADMET (absorption, distribution, metabolism, excretion and toxicity) properties but also provide some guidance on structural modifications to improve the undesired properties. In this review, we organized and compared several existing integrated web servers that can be adopted in preclinical drug development projects to evaluate the subject of interest. We also introduced our new web server, Virtual Rat, as an alternative choice to profile the properties of drug candidates. In Virtual Rat, we provide not only predictions of important ADMET properties but also possible reasons as to why the model made those structural predictions. Multiple models were implemented into Virtual Rat, including models for predicting human ether-a-go-go-related gene (hERG) inhibition, cytochrome P450 (CYP) inhibition, mutagenicity (Ames test), blood-brain barrier penetration, cytotoxicity and Caco-2 permeability. Virtual Rat is free and has been made publicly available at https://virtualrat.cmdm.tw/.

Pharmacometabonomics: data processing and statistical analysis.

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Predicting drug-disease associations through layer attention graph convolutional network.

BACKGROUND: Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug-disease associations is of great significance. RESULTS: In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug-disease association prediction. Specifically, LAGCN first integrates the known drug-disease associations, drug-drug similarities and disease-disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug-disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision-recall curve of 0.3168 and an area under the receiver-operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset. CONCLUSION: LAGCN is a useful tool for predicting drug-disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.

Recombinant Technologies Facilitate Drug Metabolism, Pharmacokinetics, and General Biomedical Research.

The development of safe and effective medications requires a profound understanding of their pharmacokinetic (PK) and pharmacodynamic properties. PK studies have been built through investigation of enzymes and transporters that drive drug absorption, distribution, metabolism, and excretion (ADME). Like many other disciplines, the study of ADME gene products and their functions has been revolutionized through the invention and widespread adoption of recombinant DNA technologies. Recombinant DNA technologies use expression vectors such as plasmids to achieve heterologous expression of a desired transgene in a specified host organism. This has enabled the purification of recombinant ADME gene products for functional and structural characterization, allowing investigators to elucidate their roles in drug metabolism and disposition. This strategy has also been used to offer recombinant or bioengineered RNA (BioRNA) agents to investigate the posttranscriptional regulation of ADME genes. Conventional research with small noncoding RNAs such as microRNAs (miRNAs) and small interfering RNAs has been dependent on synthetic RNA analogs that are known to carry a range of chemical modifications expected to improve stability and PK properties. Indeed, a novel transfer RNA fused pre-miRNA carrier-based bioengineering platform technology has been established to offer consistent and high-yield production of unparalleled BioRNA molecules from Escherichia coli fermentation. These BioRNAs are produced and processed inside living cells to better recapitulate the properties of natural RNAs, representing superior research tools to investigate regulatory mechanisms behind ADME. SIGNIFICANCE STATEMENT: This review article summarizes recombinant DNA technologies that have been an incredible boon in the study of drug metabolism and PK, providing investigators with powerful tools to express nearly any ADME gene products for functional and structural studies. It further overviews novel recombinant RNA technologies and discusses the utilities of bioengineered RNA agents for the investigation of ADME gene regulation and general biomedical research.

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to develop quantitative structure-property relationship (QSPR) models. Traditional QSPR models were trained on compound sets of limited size. With the advent of more complex ML algorithms and data availability, training sets have become larger and more diverse. Most common training approaches consist in either training a model with a small set of similar compounds, namely, compounds designed for the same drug discovery project or chemical series (local model approach) or with a larger set of diverse compounds (global model approach). Global models are built with all experimental data available for an assay, combining compound data from different projects and disease areas. Despite the ML progress made so far, the choice of the appropriate data composition for building ML models is still unclear. Herein, a systematic evaluation of local and global ML models was performed for 10 different experimental assays and 112 drug discovery projects. Results show a consistent superior performance of global models for ADME property predictions. Diagnostic analyses were also carried out to investigate the influence of training set size, structural diversity, and data shift in the relative performance of local and global ML models. Training set and structural diversity did not have an impact in the relative performance on the methods. Instead, data shift helped to identify the projects with larger performance differences between local and global models. Results presented in this work can be leveraged to improve ML-based ADME properties predictions and thus decision-making in drug discovery projects.

Assessment of the nlmixr R package for population pharmacokinetic modeling: A metformin case study.

AIM: nlmixr offers first-order conditional estimation (FOCE), FOCE with interaction (FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin's pharmacokinetic data using nlmixr and investigated SAEM and FOCEi's performance with respect to bias and precision of parameter estimates, and robustness to initial estimates. METHOD: Compartmental models were fitted. The final model was determined based on the objective function value and inspection of goodness-of-fit plots. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100 times and resultant changes evaluated. RESULTS: The absorption kinetics of metformin depend significantly on food status. Under the fasted state, the first-order absorption into the central compartment was preceded by zero-order infusion into the depot compartment, whereas for the fed state, the absorption into the depot was instantaneous followed by first-order absorption from depot into the central compartment. The means of relative mean estimation error (rMEE) ( ME E SAEM ME E FOCEi ) and rRMSE ( RMS E SAEM RMS E FOCEi ) were 0.48 and 0.35, respectively. All parameter estimates given by SAEM appeared to be narrowly distributed and were close to the true value used for simulation. In contrast, the distribution of estimates from FOCEi were skewed and more biased. When initial estimates were perturbed, FOCEi estimates were more biased and imprecise. DISCUSSION: nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations.

Pharmacokinetics under the COVID-19 storm.

AIMS: The storm-like nature of the health crises caused by COVID-19 has led to unconventional clinical trial practices such as the relaxation of exclusion criteria. The question remains: how can we conduct diverse trials without exposing subgroups of populations to potentially harmful drug exposure levels? The aim of this study was to build a knowledge base of the effect of intrinsic/extrinsic factors on the disposition of several repurposed COVID-19 drugs. METHODS: Physiologically based pharmacokinetic (PBPK) models were used to study the change in the pharmacokinetics (PK) of drugs repurposed for COVID-19 in geriatric patients, different race groups, organ impairment and drug-drug interactions (DDIs) risks. These models were also used to predict epithelial lining fluid (ELF) exposure, which is relevant for COVID-19 patients under elevated cytokine levels. RESULTS: The simulated PK profiles suggest no dose adjustments are required based on age and race for COVID-19 drugs, but dose adjustments may be warranted for COVID-19 patients also exhibiting hepatic/renal impairment. PBPK model simulations suggest ELF exposure to attain a target concentration was adequate for most drugs, except for hydroxychloroquine, azithromycin, atazanavir and lopinavir/ritonavir. CONCLUSION: We demonstrate that systematically collated data on absorption, distribution, metabolism and excretion, human PK parameters, DDIs and organ impairment can be used to verify simulated plasma and lung tissue exposure for drugs repurposed for COVID-19, justifying broader patient recruitment criteria. In addition, the PBPK model developed was used to study the effect of age and ethnicity on the PK of repurposed drugs, and to assess the correlation between lung exposure and relevant potency values from in vitro studies for SARS-CoV-2.