Neil Parrott

Accurate prediction of human pharmacokinetics is essential for selecting safe starting doses in first-in-human (FiH) trials. This study compares the predictive accuracy of three methodologies including standard physiologically based pharmacokinetic (PBPK) modeling incorporating animal verification, high-throughput (HT) PBPK based on in vitro inputs, and machine learning (ML). We evaluated their performance on 40 diverse small molecules that entered clinical development at Roche between 2003 and 2024. Standard PBPK predictions were assessed prospectively from original reports, while HT-PBPK and ML predictions were obtained retrospectively. Predicted area under the curve (AUCinf) and maximum plasma concentration (Cmax) for oral administration were compared to observed clinical data. While all three approaches demonstrated a useful level of accuracy with at least 49% of predicted AUCinf and Cmax values within 2-fold, standard PBPK was the most precise method with 65 and 63% of AUCinf and Cmax within that margin, respectively. This higher precision likely results from the integration of expert judgment and animal in vivo data for model refinement, whereas HT-PBPK and ML provide valuable animal-free, high-throughput alternatives. Ensemble models leveraging two or three models were explored, which generally improved accuracy in comparison to single models. This highlights the complementary nature of mechanistic and ML-based models. Currently, standard PBPK is still the preferred method for FiH dose selection for which highest accuracy and mechanistic insight is sought. However, although less accurate, we believe that HT-PBPK and ML are viable animal-free alternatives with value for early human dose prediction, reducing costs and cycle times.

In February of 2024, the Product Quality Research Institute held a virtual workshop entitled “Model-Informed Drug Development (MIDD) Approaches in Pediatric Formulation Development.” The workshop covered a range of topics related to pediatric formulation development and testing, including dissolution testing and applications to modeling drug absorption in developing pediatric patients. Workshop speakers reviewed the progress that has been made to advance our understanding of ontogeny related processes that drive interactions with product formulations and inform pediatric product development. They discussed recently constructed pediatric biorelevant models that provide more realistic information about drug dissolution in pediatric patients and their use in physiologically based models to produce data for regulatory submissions. Despite the progress, opportunities remain to expand the fund of knowledge on pediatric absorptive processes that will support the development of pediatric-friendly drug formulations. Graphical Abstract

Accurate prediction of the pharmacokinetic (PK) properties of small-molecule drug candidates is a critical aspect of pharmaceutical research. Fast and reliable PK predictions can accelerate compound optimization cycles, reduce animal testing, and enhance the quality of molecules advancing to human studies. Although physiologically based PK (PBPK) models are well-established for compound selection, their application in early discovery faces limitations due to low throughput and the requirement for substantial in vitro data. Recently, high-throughput PBPK (HT-PBPK) methods have become possible, offering scalable, parallel PBPK simulations that can be executed on thousands of compounds within minutes. Additionally, advancements in machine learning (ML) have enabled the substitution of in vitro data by high-quality in silico predictions that are based solely on chemical structures. In this study, the performance of a corporate HT-PBPK application, called SwiftPK, that leverages the HTPK simulation module included in a commercial software package was evaluated for predicting ten primary and secondary PK endpoints for a large (>9000 compounds) set of rodent PK data. Utilizing a corporate ML pipeline, all in vitro parameter inputs were replaced with in silico predictions. This approach is particularly relevant for early stage project phases, such as lead identification, as well as for external collaborations where experimental data are unavailable. The findings demonstrate the highly predictive performance of the HT-PBPK approach, with most endpoints predicted within a three- to four-fold error. Performance improves after filtering for compounds that are predicted, based on structure alone, to be cleared by hepatic metabolism (Extended Clearance Classification System class 2) and when using ML inputs that demonstrate high confidence. The results highlight the key prerequisites for successful application in early phase projects: predicted primary elimination pathway accuracy and prediction quality. This study is expected to inspire more organizations to incorporate HT-PBPK into their discovery pipelines, expediting the development of safe and effective novel medicines for patients.

Pediatric extrapolation strategies issued by health authorities have streamlined pediatric drug development and reduced the unnecessary burden of conducting pediatric clinical studies. In line with these strategies, physiologically based pharmacokinetic (PBPK) models have been utilized extensively for initial dosing regimen and sampling timepoint selection for pediatric studies, as well as dose validation throughout pediatric drug development. Here, the status and challenges of PBPK modeling in pediatric drug development have been summarized by the IQ Pediatric PBPK Working Group. Our work reviews current practices for pediatric PBPK modeling across various therapeutic areas. To enable best practice, we propose an optimized workflow for pediatric PBPK modeling recommendations. Two selected key pediatric PBPK case examples are also described, where modeling impacted the drug label extension to pediatric patients. Moreover, we analyze the current gaps and challenges in our understanding of drug absorption, distribution, metabolism, and elimination in pediatric PBPK model development. Since neonates are the least studied and the most medically fragile, the depth of our understanding of their rapidly evolving physiological processes is limited and so there exist significant modeling gaps which we summarize here. Finally, we provide recommendations, including building a public data repository, leveraging real‐world data, and implementing microdose studies for addressing pediatric PBPK modeling challenges.

Cytochrome P450 3A4 (CYP3A4) is a key target for time-dependent inhibition (TDI) assessment during drug development. However, translating in vitro TDI data to in vivo drug-drug interaction (DDI) risk remains challenging due to the acknowledged overestimation when incorporating in vitro kinetics in predictive models. We investigated different in vitro TDI assay conditions in human liver microsomes (HLM) and evaluated their impact on the predictive accuracy for CYP3A4-related DDI for 32 marketed drugs. Considering assay sensitivity and in vivo DDI prediction accuracy with mechanistic static modeling (MSM), optimal incubation parameters were identified as: a pre-incubation time of 40 min for precipitants and 10 min incubation time for CYP3A4 substrate midazolam (10 μM) at 0.1 mg/mL HLM. A tendency to overestimate the DDI magnitude (AFE = 4.83, AAFE = 4.87) was still observed in MSM when using the unbound drug inhibition constant (KI,_u) and maximum inactivation rate (k_inact), measured under optimized incubation conditions. Improved predictions were achieved when applying the same parameters in physiologically-based pharmacokinetic (PBPK) models (AFE = 1.94, AAFE = 2.13), with 60% of predicted AUCR falling in the twofold range. These findings highlighted the importance of optimizing in vitro TDI incubation conditions, together with evaluating the benefits and limitations of numerical prediction approaches for predicting clinically significant CYP3A4 TDI effects with mechanistic static and PBPK models. Graphical Abstract

Vicasinabin is an oral cannabinoid receptor 2 (CB2) agonist showing anti‐inflammatory effects and was developed for the treatment of chronic inflammatory diseases such as diabetic retinopathy. Vicasinabin is mainly metabolized by CYP3A4, with minor contributions from CYP2C19 and UGTs. The drug shows in vitro induction of CYP3A4, as well as inhibition of hepatic and renal transporters. Translation of in vitro data to a clinical drug–drug interaction (DDI) risk assessment has been challenging, with a potential role of CYP2C19 genotypes in the pharmacokinetics to be considered. A physiologically based pharmacokinetic (PBPK) model of vicasinabin based on a bottom‐up approach predicted a moderate systemic exposure reduction for the selective CYP3A4 substrate midazolam. Neither the OATP1B1/P‐gp/CYP3A4 inhibition effect on atorvastatin nor the OCT2/MATE1 inhibition effect on metformin was predicted to be of clinical relevance by PBPK modeling, as was confirmed by clinical DDI study data. After successful PBPK model prediction of itraconazole DDI using an in vitro fm,CYP3A4 of 0.6, the model was applied to simulate weak or moderate exposure changes of vicasinabin after co‐administration with perpetrators for CYP3A4 and CYP2C19 (erythromycin, fluconazole, fluvoxamine, efavirenz, and rifampicin). A strong effect of induction due to rifampicin was also indicated. The CYP2C19 genotypes did not result in a significant impact on the victim DDI prediction for vicasinabin due to a low fm,CYP2C19 (∼0.2). The case study illustrated the usefulness of prospective PBPK predictions of clinical drug–drug interactions using in vitro data.