Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug–drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and a new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Background and Objectives Physiologically based pharmacokinetic (PBPK) modelling has evolved to accommodate different routes of drug administration and enables prediction of drug concentrations in tissues as well as plasma. The inhalation route of administration has proven successful in treating respiratory diseases but can also be used for rapid systemic delivery, holding great promise for treatment of diseases requiring systemic exposure. The objective of this work was to develop a PBPK model that predicts plasma and tissue concentrations following inhalation administration of the PI3Kδ inhibitor nemiralisib. Methods A PBPK model was built in GastroPlus^® that includes a complete mechanistic description of pulmonary absorption, systemic distribution and oral absorption following inhalation administration of nemiralisib. The availability of clinical data obtained after intravenous, oral and inhalation administration enabled validation of the model with observed data and accurate assessment of pulmonary drug absorption. The PBPK model described in this study incorporates novel use of key parameters such as lung systemic absorption rate constants derived from human physiological lung blood flows, and implementation of the specific permeability-surface area product per millilitre of tissue cell volume (SpecPStc) to predict tissue distribution. Results The inhaled PBPK model was verified using plasma and bronchoalveolar lavage fluid concentration data obtained in human subjects. Prediction of tissue concentrations using the permeability-limited systemic disposition tissue model was further validated using tissue concentration data obtained in the rat following intravenous infusion administration to steady state. Conclusions Fully mechanistic inhaled PBPK models such as the model described herein could be applied for cross molecule assessments with respect to lung retention and systemic exposure, both in terms of pharmacology and toxicology, and may facilitate clinical indication selection.

Allometric scaling provides a practical framework for predicting human pharmacokinetic (PK) parameters from animal data by relating physiological processes to body size through power-law equations. Despite its simplicity and widespread use in first-in-human (FIH) dose selection, its predictive performance is limited by species-specific differences in absorption, distribution, metabolism, and excretion (ADME). This review summarizes the mathematical foundations, workflows, and diagnostics of allometric scaling, while critically examining where the approach succeeds and where it fails. Core concepts, including clearance, volume of distribution, correction factors, and the rule of exponents, are discussed alongside complementary methods: in vitro–in vivo extrapolation (IVIVE), physiologically based pharmacokinetic (PBPK) modelling, and the Wajima normalized time-course method. Historical clinical failures, including fialuridine, TGN1412, BIA 10-2474, and rofecoxib, illustrate the limits of relying solely on allometry, while thalidomide and the fenfluramine combination exemplify toxicodynamic species-selection failures. Modern advances, including the Extended Clearance Classification System (ECCS), target-mediated drug disposition, FcRn recycling, and emerging artificial intelligence and machine-learning methods, are integrated within a framework. Overall, the review treats allometric scaling as a disciplined starting hypothesis that must be triangulated with mechanistic, experimental, and regulatory evidence to support safer and more reliable human translation.

Background and Objective Physiologically based pharmacokinetic modelling is routinely used in the pharmaceutical industry and has an impact on drug labels. New applications have emerged, one of which is first-in-human pharmacokinetic predictions. Scientists in this field often believe that verification of models in preclinical species is essential for accurate predictions, but consensus has not been reached and animal use across the industry deserves continual examination with the aim to reduce use. Methods A published model-building strategy was used to assess the accuracy added by preclinical verification for human PK prediction. Three sequential approaches were explored using five compounds covering a range of physicochemical properties and chemical classes. Approach 1 (QSPR FIH prediction), uses parameter inputs predicted using in silico Quantitative Structure Property Relationship models; Approach 2 (In vitro FIH prediction), supplements predicted parameters with in vitro measurements where available data exist; Approach 3 (Verified FIH prediction), uses preclinical in vivo pharmacokinetic data to verify the in vitro measurements. Results Preclinical verification models were able to provide predictions of first-in-human pharmacokinetics within two-fold of the observed data for all five compounds studied, which was a significant improvement compared with predictions using in silico or in vitro inputs without verification in preclinical species. In most cases, the predictions generated purely from structure were superior to those supplemented with in vitro data without preclinical validation. Prediction of human clearance via in vitro in vivo extrapolation proved challenging and was identified as the most common cause of poor predictions of human PK from in vitro data. Conclusions The work here supports the continued considered use of preclinical verification in PBPK modelling.