Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades

Human Pharmacokinetic and CYP3A Drug-Drug Interaction Prediction of GDC-2394 Using Physiologically Based Pharmacokinetic Modeling and Biomarker Assessment

Physiologically based pharmacokinetic (PBPK) modeling was used to predict the human pharmacokinetics and drug-drug interaction (DDI) of GDC-2394. PBPK models were developed using in vitro and in vivo data to reflect the oral and intravenous PK profiles of mouse, rat, dog, and monkey. The learnings from preclinical PBPK models were applied to a human PBPK model for prospective human PK predictions. The prospective human PK predictions were within 3-fold of the clinical data from the first-in-human study, which was used to optimize and validate the PBPK model and subsequently used for DDI prediction. Based on the majority of PBPK modeling scenarios using the in vitro CYP3A induction data (mRNA and activity), GDC-2394 was predicted to have no-to-weak induction potential at 900 mg twice daily (BID). Calibration of the induction mRNA and activity data allowed for the convergence of DDI predictions to a narrower range. The plasma concentrations of the 4β-hydroxycholesterol (4β-HC) were measured in the multiple ascending dose study to assess the hepatic CYP3A induction risk. There was no change in plasma 4β-HC concentrations after 7 days of GDC-2394 at 900 mg BID. A dedicated DDI study found that GDC-2394 has no induction effect on midazolam in humans, which was reflected by the totality of predicted DDI scenarios. This work demonstrates the prospective utilization of PBPK for human PK and DDI prediction in early drug development of GDC-2394. PBPK modeling accompanied with CYP3A biomarkers can serve as a strategy to support clinical pharmacology development plans. SIGNIFICANCE STATEMENT: This work presents the application of physiologically based pharmacokinetic modeling for prospective human pharmacokinetic (PK) and drug-drug interaction (DDI) prediction in early drug development. The strategy taken in this report represents a framework to incorporate various approaches including calibration of in vitro induction data and consideration of CYP3A biomarkers to inform on the overall CYP3A-related DDI risk of GDC-2394.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

The estimation and translation uncertainties in applying NOAEL to clinical dose escalation

The systemic exposure at the no-observed-adverse-effect-level (NOAEL) estimated from animals is an important criterion commonly applied to guard the safety of participants in clinical trials of investigational drugs. However, the discrepancy in toxicity profile between species is widely recognized. The objective of the work reported here was to assess, via simulation, the level of uncertainty in the NOAEL estimated from an animal species and the effectiveness of applying its associated exposure value to minimizing the toxicity risk to human. Simulations were conducted for dose escalation of an investigational new chemical entity with hypothetical exposure-response models for the dose-limiting toxicity under a variety of conditions, in terms of between-species relative sensitivity to the toxicity and the between-subject variability in the key parameters determining the sensitivity and pharmacokinetics. Results show a high uncertainty in the NOAEL estimation. Notably, even when the animal species and humans are assumed to have the same sensitivity, which may not be realistic, limiting clinical dose to the exposure at the NOAEL that has been identified in the animals carries a high risk of either causing toxicity or under-dosing, hence undermining the therapeutic potential of the drug candidate. These findings highlight the importance of understanding the mechanism of the toxicity profile and its cross-species translatability, as well as the importance of understanding the dose requirement for achieving adequate pharmacology.

Discovery of GLPG2737, a Potent Type 2 Corrector of CFTR for the Treatment of Cystic Fibrosis in Combination with a Potentiator and a Type 1 Co-corrector

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator (CFTR) protein. This epithelial anion channel regulates the active transport of chloride and bicarbonate ions across membranes. Mutations result in reduced surface expression of CFTR channels with impaired functionality. Correctors are small molecules that support the trafficking of CFTR to increase its membrane expression. Such correctors can have different mechanisms of action. Combinations may result in a further improved therapeutic benefit. We describe the identification and optimization of a new pyrazolol3,4-bl pyridine-6-carboxylic acid series with high potency and efficacy in rescuing CFTR from the cell surface. Investigations showed that carboxylic acid group replacement with acylsulfonamides and acylsulfonylureas improved ADMET and PK properties, leading to the discovery of the structurally novel co-corrector GLPG2737. The addition of GLPG2737 to the combination of the potentiator GLPG1837 and C1 corrector 4 led to an 8-fold increase in the F508del CFTR activity.

Pharmacokinetic considerations for enhancing drug repurposing opportunities of anthelmintics: Niclosamide as a case study

Recently, anthelmintics have showcased versatile therapeutic potential in addressing various diseases, positioning them as promising candidates for drug repurposing. However, challenges such as low bioavailability and a lack of a solid pharmacokinetic basis impede successful repurposing. To overcome these flaws, we aimed to investigate the key pharmacokinetic factors of anthelmintics mainly focusing on the absorption, distribution, and metabolism profiles by employing niclosamide (NIC) as a model drug. The intestinal permeability of NIC is significantly influenced by solubility and doesn't function as a substrate for efflux transporters. It showed high plasma protein binding. Also, the metabolism study indicated that NIC would have low metabolic stability by extensively undergoing the intestinal glucuronidation. Additionally, we investigated the CYP-mediated drug-drug interaction potential of NIC in both direct and time-dependent ways. NIC showed strong inhibitory effects on CYP1A2 and CYP2C8 and is not likely to become a time-dependent inhibitor. Our findings could contribute to the identification of essential factors in the pharmacokinetics of anthelmintics, potentially facilitating their repositioning.

An Ultrasoft and Flexible PDMS-Based Balloon-Type Implantable Device for Controlled Drug Delivery

Non-biodegradable implants have undergone extensive investigation as drug delivery devices to enable advanced healthcare toward personalized medicine. However, fibroblast encapsulation is one of the major challenges in all non-biodegradable implants, besides other challenges such as high initial burst, risk of membrane rupture, high onset time, non-conformal contact with tissues, and tissue damage. To tackle such challenges, we propose a novel ultrasoft and flexible balloon-type drug delivery device for unidirectional and long-term controlled release. The ultrasoft balloon-type device (USBD) was fabricated by using selective bonding between 2 polydimethylsiloxane (PDMS) membranes and injecting a fluid into the non-bonded area between them. The balloon acted as a reservoir containing a liquid drug, and at the same time, the membrane of the balloon itself acted as the pathway for release based on diffusion. The release was modulated by tuning the thickness and composition of the PDMS membrane. Regardless of the thickness and composition, all devices exhibited zero-order release behavior. The longest zero-order release and nearly zero-order release were achieved for 30 days and 58 days at a release rate of 1.16 μg/day and 1.68 μg/day, respectively. In vivo evaluation was performed for 35 days in living rats, where the USBD maintained zero-order and nearly zero-order release for 28 days and 35 days, respectively. Thanks to the employment of ultrasoft and flexible membranes and device design, the USBD could achieve minimal tissue damage and foreign body responses. It is expected that the proposed device may provide a novel approach for long-term drug delivery with new therapeutic modalities.

Cross-Species Extrapolation of Biological Data to Guide the Environmental Safety Assessment of Pharmaceuticals-The State of the Art and Future Priorities

The extrapolation of biological data across species is a key aspect of biomedical research and drug development. In this context, comparative biology considerations are applied with the goal of understanding human disease and guiding the development of effective and safe medicines. However, the widespread occurrence of pharmaceuticals in the environment and the need to assess the risk posed to wildlife have prompted a renewed interest in the extrapolation of pharmacological and toxicological data across the entire tree of life. To address this challenge, a biological "read-across" approach, based on the use of mammalian data to inform toxicity predictions in wildlife species, has been proposed as an effective way to streamline the environmental safety assessment of pharmaceuticals. Yet, how effective has this approach been, and are we any closer to being able to accurately predict environmental risk based on known human risk? We discuss the main theoretical and experimental advancements achieved in the last 10 years of research in this field. We propose that a better understanding of the functional conservation of drug targets across species and of the quantitative relationship between target modulation and adverse effects should be considered as future research priorities. This pharmacodynamic focus should be complemented with the application of higher-throughput experimental and computational approaches to accelerate the prediction of internal exposure dynamics. The translation of comparative (eco)toxicology research into real-world applications, however, relies on the (limited) availability of experts with the skill set needed to navigate the complexity of the problem; hence, we also call for synergistic multistakeholder efforts to support and strengthen comparative toxicology research and education at a global level. Environ Toxicol Chem 2024;43:513-525. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Human Dose and Pharmacokinetic Predictions for Biologics at Boehringer Ingelheim: A Retrospective Analysis

INTRODUCTION

Accurate predictions of pharmacokinetics and efficacious doses for biologics in humans are critical for selecting appropriate first-in-human starting doses and dose ranges and for estimating clinical material needs and cost of goods. This also impacts clinical feasibility, particularly for subcutaneously administered biologics.

METHODS

We performed a comprehensive comparison between predicted and observed clearances and doses in humans for a set of 22 biologic drugs developed at Boehringer Ingelheim (BI) over the last 2 decades. The analysis included biologics across three therapeutic areas comprising a wide variety of modalities: mono- and bispecific monoclonal antibodies (mAbs) and nanobodies and a Fab fragment.

RESULTS

Our analysis showed that observed clearances in humans were within twofold of predicted clearances for 17 out of 20 biologics (85%). Six biologics had uncharacteristically high observed human clearances (range 32-280 mL/h) for their respective molecular classes, impacting their clinical developability. For three molecules, molecular characteristics contributed to the high clearance. Clinically selected doses were within twofold of predicted for 58% of projects. With 42% and 25% of projects selecting clinical doses higher than two- or threefold the predicted value, respectively, the importance of better understanding not only the pharmacokinetic (PK) but also the predictivity of pharmacodynamic models is highlighted.

CONCLUSIONS

We provide a clinical pharmacology perspective on the commonly accepted twofold range of human clearance predictions as well as the implications of higher than predicted targeted efficacious plasma concentration on clinical development. Finally, an analysis of key success factors for biologics at BI was conducted, which may be relevant for the entire pharmaceutical industry. This is one of the largest retrospective analyses for biologics and provides further evidence that successful predictions of human PK and efficacious dose will be further facilitated by gathering key translational data early in research.

Approaches to Account for Colon Absorption in Physiologically Based Biopharmaceutics Modeling of Extended-Release Drug Products

The rate and extent of colon absorption are important determinants of the in vivo performance of extended-release (ER) drug products. The ability to appropriately predict this at different stages of development using mechanistic physiologically based biopharmaceutic modeling (PBBM) is highly desirable. This investigation aimed to evaluate the prediction performance of three different approaches to account for colon absorption in predictions of the in vivo performance of ER drug product variants with different in vitro release profiles. This was done by mechanistic predictions of the absorption and plasma exposure of the ER drug products using GastroPlus and GI-Sim for five drugs with different degrees of colon absorption limitations in humans. Colon absorption was accounted for in the predictions using three different approaches: (1) by an a priori approach using the default colon models, (2) by fitting the colon absorption scaling factors to the observed plasma concentration-time profiles after direct administration to the colon in humans, or (3) from the ER drug product variant with the slowest in vitro release profile. The prediction performance was evaluated based on the percentage prediction error and the average absolute prediction error (AAPE). Two levels of acceptance criteria corresponding to highly accurate (AAPE ≤ 20%) and accurate (AAPE 20-50%) predictions were defined prior to the evaluation. For the a priori approach, the relative bioavailability (Frel), AUC0-t, and Cmax of the ER drug product variants for the low to medium colon absorption limitation risk drugs was accurately predicted with an AAPE range of 11-53 and 8-59% for GastroPlus and GI-Sim, respectively. However, the prediction performance was poor for the high colon absorption limitation risk drugs. Moreover, accounting for the human regional colon absorption data in the models did not improve the prediction performance. In contrast, using the colon absorption scaling factors derived from the slowest ER variant significantly improved the prediction performance regardless of colon absorption limitation, with a majority of the predictions meeting the high accuracy criteria. For the slowest ER approach, the AAPE ranges were 5-24 and 5-32% for GastroPlus and GI-Sim, respectively, excluding the low permeability drug. In conclusion, the a priori PBBM can be used during candidate selection and early product design to predict the in vivo performance of ER drug products for low to medium colon absorption limitation risk drugs with sufficient accuracy. The results also indicate a limited value in performing human regional absorption studies in which the drug is administered to the colon as a bolus to support PBBM development for ER drug products. Instead, by performing an early streamlined relative bioavailability study with the slowest relevant ER in vitro release profile, a highly accurate PBBM suitable for ER predictions for commercial and regulatory applications can be developed, except for permeability-limited drugs.

In silico prediction of bioequivalence of atorvastatin tablets based on GastroPlus™ software

The prediction of intestinal absorption of various drugs based on computer simulations has been a reality. However, in vivo pharmacokinetic simulations and virtual bioequivalence evaluation based on GastroPlus™ have not been found. This study aimed to simulate plasma concentrations with different dissolution profiles and run population simulations to evaluate the bioequivalence of test and reference products of atorvastation using GastroPlus software. The dissolution profiles of the reference and test products of atorvastatin (20 mg tablets), and clinical plasma concentration-time data of the reference product were used for the simulations. The results showed that the simulated models were successfully established for atorvastatin tablets. Population simulation results indicated that the test formulation was bioequivalent to the reference formulation. The findings suggest that modelling is an essential tool to demonstrating the possibility of pharmacokinetic and bioequivalence for atorvastatin. It will contribute to understanding the potential risks during the development of generic products.

Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Towards single cell encapsulation for precision biology and medicine

The primary impetus of therapeutic cell encapsulation in the past several decades has been to broaden the options for donor cell sources by countering against immune-mediated rejection. However, another significant advantage of encapsulation is to provide donor cells with physiologically relevant cues that become compromised in disease. The advances in biomaterial design have led to the fundamental insight that cells sense and respond to various signals encoded in materials, ranging from biochemical to mechanical cues. The biomaterial design for cell encapsulation is becoming more sophisticated in controlling specific aspects of cellular phenotypes and more precise down to the single cell level. This recent progress offers a paradigm shift by designing single cell-encapsulating materials with predefined cues to precisely control donor cells after transplantation.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning

MOTIVATION

Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice. However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability.

RESULTS

To address these issues, we develop a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of drug candidates. In our framework, we design deep learning methods to extract features for molecules from two modality data, i.e. SMILES sequence and molecule graph. In particular, for the sequence data, we design a multihead attention BiGRU-based encoder to preserve the context of symbols to learn sequence representations of molecules. For the graph data, we propose a graph contrastive learning-based encoder to learn structure representations by effectively capturing the consistencies between local and global structures. We further exploit fully connected neural networks to combine the sequence and structure representations for model training. Extensive experimental results on two datasets demonstrate that our CMMS-GCL consistently outperforms seven state-of-the-art methods. Furthermore, a collection of case studies on sequence data and statistical analyses of the graph structure module strengthens the validation of the interpretability of crucial functional groups recognized by CMMS-GCL. Overall, CMMS-GCL can serve as an effective and interpretable tool for predicting metabolic stability, identifying critical functional groups, and thus facilitating the drug discovery process and lead compound optimization.

AVAILABILITY AND IMPLEMENTATION

The code and data underlying this article are freely available at https://github.com/dubingxue/CMMS-GCL.

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

How effective are ionization state-based QSPKR models at predicting pharmacokinetic parameters in humans?

Optimizing the pharmacokinetics (PK) of a drug candidate to support oral dosing is a key challenge in drug development. PK parameters are usually estimated from the concentration-time profile following intravenous administration; however, traditional methods are time-consuming and expensive. In recent years, quantitative structure-pharmacokinetic relationship (QSPKR), an in silico tool that aims to develop a mathematical relationship between the structure of a molecule and its PK properties, has emerged as a useful alternative to experimental testing. Due to the complex nature of the various processes involved in dictating the fate of a drug, the development of adequate QSPKR models that can be used in real-world pre-screening situations has proved challenging. Given the crucial role played by a molecule's ionization state in determining its PK properties, this work aims to build predictive QSPKR models for PK parameters in humans using an ionization state-based strategy. We divide a high-quality dataset into clusters based on ionization state at physiological pH and build global and ion subset-based 'local' models for three major PK parameters: plasma clearance (CL), steady-state volume of distribution (VDss), and half-life (t1/2). We use a robust methodology developed in our lab entitled 'EigenValue ANalySis' that accounts for the stereospecificity in drug disposition and use the support vector machine algorithm for model building. Our findings suggest that categorizing compounds in accordance with ionization state does not result in improved QSPKR models. The narrow ranges in the endpoints along with redundancies in the data adversely affect the ion subset-based QSPKR models. We suggest alternative approaches such as elimination route-based models that account for drug-transporter interactions for CL and chemotype-specific QSPKR for VDss.

Molecular modeling study of micro and nanocurcumin with in vitro and in vivo antibacterial validation

Repurposing natural compounds as inhibitory targets to combat bacterial virulence is an important potential strategy to overcome resistance to traditional antibiotics, in the present study, the antibacterial activity of micro-curcumin and nano-sized curcumin was investigated against four predominant bacterial pathogens, namely, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Curcumin bactericidal susceptibility could be summarized as the order, P. aeruginosa > B. subtilis > S. aureus > E. coli. Molecular docking analysis was conducted to confirm the impact of curcumin on the most vital and positively identified quorum-sensing pathway signaling proteins SecA-SecY, LsrR, PqsR (MvfR), AgrA which act as key players in the bacterial communication systems. The in silico physicochemical properties revealed that curcumin as a nutraceutical can be classified as a drug-like compound. An in vivo infected wound model was employed in four groups of albino rats. Topical application of nano-curcumin lotion showed a marked reduction in wound area (98.8%) as well as nearly 100% reduction in total bacterial viable count compared to the control group, on the fifteenth day post-treatment post-injury. The obtained data suggested that curcumin nanoparticles exhibited superior antibacterial activity and may possess clinical utility as a novel topical antimicrobial and wound healing agent.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL_int) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF_lin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL_int. These SFs contain both SF_lin and an exponential SF (SF_β) of fraction unbound in plasma (f_u,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CL_int, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CL_int. This observation is different than in rats, where rat liver microsomal CL_int correlates well with in vivo CL_int for compounds demonstrating permeability-limited metabolism. The good CL_int IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

Physiologically Based Biopharmaceutics Modeling of regional and colon absorption in humans

Colon absorption is a key determinant for successful development of extended release and colon targeted drug products. This is the first systematic evaluation of the ability to predict in vivo regional differences in absorption and the extent of colon absorption in humans using mechanistic physiologically based biopharmaceutics modeling (PBBM). A new dataset, consisting of 19 drugs with a wide range of biopharmaceutics properties and extent of colon absorption in humans, was established. Mechanistic predictions of the extent of absorption and plasma exposure after oral, or jejunal and direct colon administration were performed in GastroPlus and GI-Sim using an a priori approach. Two new colon models developed in GI-Sim, were also evaluated to assess if the prediction performance could be improved. Both GastroPlus and GI-Sim met the pre-defined criteria for accurate predictions of regional and colon absorption for high permeability drugs irrespective of formulation type, while the prediction performance was poor for low permeability drugs. For solutions, the two new GI-Sim colon models improved the colon absorption prediction performance for the low permeability drugs while maintaining the accurate prediction performance for the high permeability drugs. In contrast, the prediction performance decreased for non-solutions using the two new colon models. In conclusion, PBBM can be used with sufficient accuracy to predict regional and colon absorption in humans for high permeability drugs in candidate selection as well as early design and development of extended release or colon targeted drug products. The prediction performance of the current models needs to be improved to allow high accuracy predictions for commercial drug product applications including highly accurate predictions of the entire plasma concentration-time profiles as well as for low permeability drugs.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Artificial intelligence for compound pharmacokinetics prediction

Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learning and artificial intelligence (AI) emerged as a major tool for modelling of in vivo animal and human PK, enabling prediction from chemical structure early in drug discovery, and therefore offering opportunities to guide the design and prioritisation of molecules based on relevant in vivo properties and, ultimately, predicting human PK at the point of design. This review presents recent advances in machine learning and AI models for in vivo animal and human PK for small-molecule compounds as well as some examples for antibody therapeutics.

Opportunities for Accelerating Drug Discovery and Development by Using Engineered Drug-Metabolizing Enzymes

The study of drug metabolism is fundamental to drug discovery and development (DDD) since by mediating the clearance of most drugs, metabolic enzymes influence their bioavailability and duration of action. Biotransformation can also produce pharmacologically active or toxic products, which complicates the evaluation of the therapeutic benefit versus liability of potential drugs but also provides opportunities to explore the chemical space around a lead. The structures and relative abundance of metabolites are determined by the substrate and reaction specificity of biotransformation enzymes and their catalytic efficiency. Preclinical drug biotransformation studies are done to quantify in vitro intrinsic clearance to estimate likely in vivo pharmacokinetic parameters, to predict an appropriate dose, and to anticipate interindividual variability in response, including from drug-drug interactions. Such studies need to be done rapidly and cheaply, but native enzymes, especially in microsomes or hepatocytes, do not always produce the full complement of metabolites seen in extrahepatic tissues or preclinical test species. Furthermore, yields of metabolites are usually limiting. Engineered recombinant enzymes can make DDD more comprehensive and systematic. Additionally, as renewable, sustainable, and scalable resources, they can also be used for elegant chemoenzymatic, synthetic approaches to optimize or synthesize candidates as well as metabolites. Here, we will explore how these new tools can be used to enhance the speed and efficiency of DDD pipelines and provide a perspective on what will be possible in the future. The focus will be on cytochrome P450 enzymes to illustrate paradigms that can be extended in due course to other drug-metabolizing enzymes. SIGNIFICANCE STATEMENT: Protein engineering can generate enhanced versions of drug-metabolizing enzymes that are more stable, better suited to industrial conditions, and have altered catalytic activities, including catalyzing non-natural reactions on structurally complex lead candidates. When applied to drugs in development, libraries of engineered cytochrome P450 enzymes can accelerate the identification of active or toxic metabolites, help elucidate structure activity relationships, and, when combined with other synthetic approaches, provide access to novel structures by regio- and stereoselective functionalization of lead compounds.

User-designed device with programmable release profile for localized treatment

Three-dimensional printing enables precise and on-demand manufacture of customizable drug delivery systems to advance healthcare toward the goal of personalized medicine. However, major challenges remain in realizing personalized drug delivery that fits a patient-specific drug dosing schedule using local drug delivery systems. In this study, a user-designed device is developed as implantable therapeutics that can realize personalized drug release kinetics by programming the inner structural design on the microscale. The drug release kinetics required for various treatments, including dose-dense therapy and combination therapy, can be implemented by controlling the dosage and combination of drugs along with the rate, duration, initiation time, and time interval of drug release according to the device layer design. After implantation of the capsular device in mice, the in vitro-in vivo and pharmacokinetic evaluation of the device is performed, and the therapeutic effect of the developed device is achieved through the local release of doxorubicin. The developed user-designed device provides a novel platform for developing next-generation drug delivery systems for personalized and localized therapy.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.

Synthesis and Anti-Inflammatory Activity of 2-Amino-4,5,6,7-tetrahydrobenzo[b]thiophene-Derived NRF2 Activators

This is the first study investigating the nuclear factor (erythroid‐derived 2)‐like 2 (NRF2) activity of compounds containing a new scaffold, tetrahydrobenzo[b]thiophene. Eighteen compounds were synthesised and confirmed their NRF2 activation through NQO1 enzymatic activity and mRNA expression of NQO1 and HO‐1 in Hepa‐1c1c7 cells. The compounds disrupted the interaction between Kelch‐like ECH‐associated protein 1 (KEAP1) and NRF2 via interfering with the KEAP1’s Kelch domain. The compounds exhibited anti‐inflammatory activity in Escherichia coli Lipopolysaccharide (LPS_Ec)‐stimulated RAW 264.7 cells. The anti‐inflammatory activity of the compounds was associated with their ability to activate NRF2. The compounds reversed the elevated levels of pro‐inflammatory cytokines (IL‐1β, IL‐6, TNF‐α, and IFN‐γ) and inflammatory mediators (PGE2, COX‐2, and NF‐κB). The compounds were metabolically stable in human, rat, and mouse liver microsomes and showed optimum half‐life (T_1/2) and intrinsic clearance (Cl_int). The binding mode of the compounds and physicochemical properties were predicted via in silico studies.

ADME and DMPK considerations for the discovery and development of antibody drug conjugates (ADCs)

 The therapeutic concept of antibody drug conjugates (ADCs) is to selectively target tumour cells with small molecule cytotoxic drugs to maximise cell kill benefit and minimise healthy tissue toxicity.An ADC generally consists of an antibody that targets a protein on the surface of tumour cells chemically linked to a warhead small molecule cytotoxic drug.To deliver the warhead to the tumour cell, the antibody must bind to the target protein and in general be internalised into the cell. Following internalisation, the cytotoxic agent can be released in the endosomal or lysosomal compartment (via different mechanisms). Diffusion or transport out of the endosome or lysosome allows the cytotoxic drug to express its cell-killing pharmacology. Alternatively, some ADCs (e.g. EDB-ADCs) rely on extracellular cleavage releasing membrane permeable warheads.One potentially important aspect of the ADC mechanism is the 'bystander effect' whereby the cytotoxic drug released in the targeted cell can diffuse out of that cell and into other (non-target expressing) tumour cells to exert its cytotoxic effect. This is important as solid tumours tend to be heterogeneous and not all cells in a tumour will express the targeted protein.The combination of large and small molecule aspects in an ADC poses significant challenges to the disposition scientist in describing the ADME properties of the entire molecule.This article will review the ADC landscape and the ADME properties of successful ADCs, with the aim of outlining best practice and providing a perspective of how the field can further facilitate the discovery and development of these important therapeutic modalities.

Why 90% of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies, which raised the question whether certain aspects in target validation and drug optimization are overlooked? Current drug optimization overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity in disease/normal tissues using structure‒tissue exposure/selectivity-relationship (STR), which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity. We propose structure‒tissue exposure/selectivity-activity relationship (STAR) to improve drug optimization, which classifies drug candidates based on drug's potency/selectivity, tissue exposure/selectivity, and required dose for balancing clinical efficacy/toxicity. Class I drugs have high specificity/potency and high tissue exposure/selectivity, which needs low dose to achieve superior clinical efficacy/safety with high success rate. Class II drugs have high specificity/potency and low tissue exposure/selectivity, which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated. Class III drugs have relatively low (adequate) specificity/potency but high tissue exposure/selectivity, which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked. Class IV drugs have low specificity/potency and low tissue exposure/selectivity, which achieves inadequate efficacy/safety, and should be terminated early. STAR may improve drug optimization and clinical studies for the success of clinical drug development.

Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development

Drug metabolism and pharmacokinetics (DMPK) is an important branch of pharmaceutical sciences. The nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has evolved in recent years from being largely descriptive to seeking a more quantitative and mechanistic understanding of the fate of drug candidates in biological systems. Tremendous progress has been made in the past decade, not only in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity, but also in the identification of design principles that can minimize drug-drug interaction (DDI) potentials and reduce the attritions. The importance of membrane transporters in drug disposition, efficacy, and safety, as well as the interplay with metabolic processes, has been increasingly recognized. Dramatic increases in investments on new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and antibody-drug conjugates, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME properties. In this review, we highlight some of the most notable advances in the last decade, and provide future perspectives on potential major breakthroughs and innovations in the translation of DMPK science in various stages of drug discovery and development.

Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images

Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed up the progression of novel candidate molecules. Herein, we have investigated the question if multiple in vitro clearance endpoints could be accurately predicted from image-based molecular representations. Thus, compound measurements for four commonly investigated clearance endpoints were curated from AstraZeneca internal sources, providing a sound basis for building multi-task convolutional neural network models. Application of several increasingly challenging data splitting strategies confirmed that convolutional neural network models were successful at capturing implicit chemical relationships contained in training and test data, similar to what is commonly observed for structural fingerprints. Furthermore, model benchmarking against state-of-the-art machine learning methods, including deep neural networks and graph convolutional neural networks, trained with structure- and graph-based representations, respectively, revealed on par or increased accuracy of convolutional neural networks with clear benefit of multi-task learning across all clearance endpoints. Our findings indicate that image-based molecular representations can be applied to predict multiple clearance endpoints, suggesting a potential follow-up to investigate model interpretability from molecular images.

Structure‒tissue exposure/selectivity relationship (STR) correlates with clinical efficacy/safety

Drug optimization, which improves drug potency/specificity by structure‒activity relationship (SAR) and drug-like properties, is rigorously performed to select drug candidates for clinical trials. However, the current drug optimization may overlook the structure‒tissue exposure/selectivity-relationship (STR) in disease-targeted tissues vs. normal tissues, which may mislead the drug candidate selection and impact the balance of clinical efficacy/toxicity. In this study, we investigated the STR in correlation with observed clinical efficacy/toxicity using seven selective estrogen receptor modulators (SERMs) that have similar structures, same molecular target, and similar/different pharmacokinetics. The results showed that drug's plasma exposure was not correlated with drug's exposures in the target tissues (tumor, fat pad, bone, uterus), while tissue exposure/selectivity of SERMs was correlated with clinical efficacy/safety. Slight structure modifications of four SERMs did not change drug's plasma exposure but altered drug's tissue exposure/selectivity. Seven SERMs with high protein binding showed higher accumulation in tumors compared to surrounding normal tissues, which is likely due to tumor EPR effect of protein-bound drugs. These suggest that STR alters drug's tissue exposure/selectivity in disease-targeted tissues vs. normal tissues impacting clinical efficacy/toxicity. Drug optimization needs to balance the SAR and STR in selecting drug candidate for clinical trial to improve success of clinical drug development.

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R² = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R² = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review

The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Machine Learning Models for Human In Vivo Pharmacokinetic Parameters with In-House Validation

Prior to clinical development, a comprehensive pharmacokinetic characterization of a novel drug is required to understand its exposure at the site of action and elimination. Accordingly, in vitro assays and animal pharmacokinetic studies are regularly employed to predict drug exposure in humans, which is often costly and time-consuming. For this reason, the prediction of human pharmacokinetics at the point of design would be of high value for drug discovery. Therefore, we have established a comprehensive data curation protocol that enables machine learning evaluation of 12 human in vivo pharmacokinetic parameters using only chemical structure information and available doses for 1001 unique compounds. These machine learning models were thoroughly investigated and validated using both an independent hold-out test set and AstraZeneca clinical data. In addition, the availability of preclinical predictions for a subset of internal clinical candidates allowed us to compare our in silico approach with state-of-the-art pharmacokinetic predictions. Based on this evaluation, three fit-for-purpose models for AUC PO (R_test² = 0.63; RMSE_test = 0.76), C_max PO (R_test² = 0.68; RMSE_test = 0.62), and Vd_ss IV (R_test² = 0.47; RMSE_test = 0.50) were identified. Based on the findings, our machine learning models have considerable potential for practical applications in drug discovery, such as influencing decision-making in drug discovery projects and progression of drug candidates toward the clinic.

Active Learning for Drug Design: A Case Study on the Plasma Exposure of Orally Administered Drugs

The success of artificial intelligence (AI) models has been limited by the requirement of large amounts of high-quality training data, which is just the opposite of the situation in most drug discovery pipelines. Active learning (AL) is a subfield of AI that focuses on algorithms that select the data they need to improve their models. Here, we propose a two-phase AL pipeline and apply it to the prediction of drug oral plasma exposure. In phase I, the AL-based model demonstrated a remarkable capability to sample informative data from a noisy data set, which used only 30% of the training data to yield a prediction capability with an accuracy of 0.856 on an independent test set. In phase II, the AL-based model explored a large diverse chemical space (855K samples) for experimental testing and feedback. Improved accuracy and new highly confident predictions (50K samples) were observed, which suggest that the model's applicability domain has been significantly expanded.

The role of DMPK science in improving pharmaceutical research and development efficiency

The successful regulatory authority approval rate of drug candidates in the drug development pipeline is crucial for determining pharmaceutical research and development (R&D) efficiency. Regulatory authorities include the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceutical and Food Safety Bureau Japan (PFSB), among others. Optimal drug metabolism and pharmacokinetics (DMPK) properties influence the progression of a drug candidate from the preclinical to the clinical phase. In this review, we provide a comprehensive assessment of essential concepts, methods, improvements, and challenges in DMPK science and its significance in drug development. This information provides insights into the association of DMPK science with pharmaceutical R&D efficiency.

Artificial Intelligence in Drug Safety and Metabolism

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.

An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.

How translational modeling in oncology needs to get the mechanism just right

Translational model‐based approaches have played a role in increasing success in the development of novel anticancer treatments. However, despite this, significant translational uncertainty remains from animal models to patients. Optimization of dose and scheduling (regimen) of drugs to maximize the therapeutic utility (maximize efficacy while avoiding limiting toxicities) is still predominately driven by clinical investigations. Here, we argue that utilizing pragmatic mechanism‐based translational modeling of nonclinical data can further inform this optimization. Consequently, a prototype model is demonstrated that addresses the required fundamental mechanisms.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

Healthy volunteers in first-in-human oncology drug development for small molecules

This review provides tools to consider the inclusion of healthy volunteers (HVs) in first-in-human (FIH) oncology clinical trials with small molecules, including targeted and immunomodulatory agents, a strategy that was not envisioned with classic chemotherapy. To enable an FIH oncology trial in HVs compared to cancer patients (CPs), a robust nonclinical package must be generated, which includes toxicokinetic and pharmacokinetic studies, as well as more extensive safety pharmacology, toxicology and genotoxicity studies. This strategy could provide an early clinical characterization of the pharmacokinetic parameters and clinical safety profile in the absence of comorbidities and concomitant medication. It also avoids the ethical issue of administrating subtherapeutic doses to CPs, and could potentially help to accelerate the timelines of clinical drug development for patient care. That being said, stakeholders involved in these studies need to proceed with caution, fully understand the regulatory guidance and thoroughly evaluate the benefits and risks. This paper serves to address the regulatory guidance and other considerations needed when using healthy volunteers in early oncology trials.

Machine learning and big data provide crucial insight for future biomaterials discovery and research

Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.

Let's talk about sex: Differences in drug therapy in males and females

Professor Henry Higgins in My Fair Lady said, 'Why can't a woman be more like a man?' Perhaps unintended, such narration extends to the reality of current drug development. A clear sex-gap exists in pharmaceutical research spanning from preclinical studies, clinical trials to post-marketing surveillance with a bias towards males. Consequently, women experience adverse drug reactions from approved drug products more often than men. Distinct differences in pharmaceutical response across drug classes and the lack of understanding of disease pathophysiology also exists between the sexes, often leading to suboptimal drug therapy in women. This review explores the influence of sex as a biological variable in drug delivery, pharmacokinetic response and overall efficacy in the context of pharmaceutical research and practice in the clinic. Prospective recommendations are provided to guide researchers towards the consideration of sex differences in methodologies and analyses. The promotion of disaggregating data according to sex to strengthen scientific rigour, encouraging innovation through the personalisation of medicines and adopting machine learning algorithms is vital for optimised drug development in the sexes and population health equity.

Dose selection for intracerebroventricular cerliponase alfa in children with CLN2 disease, translation from animal to human in a rare genetic disease

Neuronal ceroid lipofuscinosis type 2 (CLN2 disease) is an ultra‐rare pediatric neurodegenerative disorder characterized by deficiency of the lysosomal enzyme tripeptidyl peptidase‐1 (TPP1). In the absence of adequate TPP1, lysosomal storage material accumulation occurs in the central nervous system (CNS) accompanied by neurodegeneration and neurological decline that culminates in childhood death. Cerliponase alfa is a recombinant human TPP1 enzyme replacement therapy administered via intracerebroventricular infusion and approved for the treatment of CLN2 disease. Here, we describe two allometric methods, calculated by scaling brain mass across species, that informed the human dose selection and exposure prediction of cerliponase alfa from preclinical studies in monkeys and a dog model of CLN2 disease: (1) scaling of dose using a human‐equivalent dose factor; and (2) scaling of compartmental pharmacokinetic (PK) model parameters. Source PK data were obtained from cerebrospinal fluid (CSF) samples from dogs and monkeys, and the human exposure predictions were confirmed with CSF data from the first‐in‐human clinical study. Nonclinical and clinical data were analyzed using noncompartmental analysis and nonlinear mixed‐effect modeling approaches. Both allometric methods produced CSF exposure predictions within twofold of the observed exposure parameters maximum plasma concentration (C_max) and area under the curve (AUC). Furthermore, cross‐species qualification produced consistent and reasonable PK profile predictions, which supported the allometric scaling of model parameters. The challenges faced in orphan drug development place an increased importance on, and opportunity for, data translation from research and nonclinical development. Our approach to dose translation and human exposure prediction for cerliponase alfa may be applicable to other CNS administered therapies being developed.

Can Natural Products Exert Neuroprotection without Crossing the Blood-Brain Barrier?

The scope of evidence on the neuroprotective impact of natural products has been greatly extended in recent years. However, a key question that remains to be answered is whether natural products act directly on targets located in the central nervous system (CNS), or whether they act indirectly through other mechanisms in the periphery. While molecules utilized for brain diseases are typically bestowed with a capacity to cross the blood–brain barrier, it has been recently uncovered that peripheral metabolism impacts brain functions, including cognition. The gut–microbiota–brain axis is receiving increasing attention as another indirect pathway for orally administered compounds to act on the CNS. In this review, we will briefly explore these possibilities focusing on two classes of natural products: omega-3 polyunsaturated fatty acids (n-3 PUFAs) from marine sources and polyphenols from plants. The former will be used as an example of a natural product with relatively high brain bioavailability but with tightly regulated transport and metabolism, and the latter as an example of natural compounds with low brain bioavailability, yet with a growing amount of preclinical and clinical evidence of efficacy. In conclusion, it is proposed that bioavailability data should be sought early in the development of natural products to help identifying relevant mechanisms and potential impact on prevalent CNS disorders, such as Alzheimer’s disease.