The application of physiologically based pharmacokinetic (PBPK) modeling has developed rapidly within the pharmaceutical industry and is becoming an integral part of drug discovery and development. In this study, we provide a cross pharmaceutical industry position on "how PBPK modeling can be applied in industry" focusing on the strategies for application of PBPK at different stages, an associated perspective on the confidence and challenges, as well as guidance on interacting with regulatory agencies and internal best practices.

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

The validation of the generic PBPK model built in-house demonstrated that as long as the absorption profile of a compound is determined solely by solubility and paracellular or transcellular permeability, the PBPK simulations of oral profiles using optimized parameters from intravenous simulations provide reasonably good agreement with the observed profile with respect to both the lineshape fit and prediction of pharmacokinetic parameters. Therefore, any lineshape mismatch between PBPK simulated and observed oral profiles can be interpreted suitably to gain mechanistic insights into the pharmacokinetic processes that have resulted in the observed lineshape. A strategy has been proposed to identify involvement of carrier-mediated transport; clearance saturation; enterohepatic recirculation of the parent compound; extra-hepatic, extra-gut elimination; higher in vivo solubility than predicted in vitro; drug-induced gastric emptying delays; gut loss and regional variation in gut absorption.

The predictive performance of physiologically‐based pharmacokinetics (PBPK) models for pharmacokinetics (PK) in renal impairment (RI) and hepatic impairment (HI) populations was evaluated using clinical data from 29 compounds with 106 organ impairment study arms were collected from 19 member companies of the International Consortium for Innovation and Quality in Pharmaceutical Development. Fifty RI and 56 HI study arms with varying degrees of organ insufficiency along with control populations were evaluated. For RI, the area under the curve (AUC) ratios of RI to healthy control were predicted within twofold of the observed ratios for > 90% (N = 47/50 arms). For HI, > 70% (N = 43/56 arms) of the hepatically impaired to healthy control AUC ratios were predicted within twofold. Inaccuracies, typically overestimation of AUC ratios, occurred more in moderate and severe HI. PBPK predictions can help determine the need and timing of organ impairment study. It may be suitable for predicting the impact of RI on PK of drugs predominantly cleared by metabolism with varying contribution of renal clearance. PBPK modeling may be used to support mild impairment study waivers or clinical study design.

When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.

Purpose: Evidence has accumulated in recent years suggestive of a genetic basis for a susceptibility to the development of radiation injury after cancer radiotherapy. The purpose of this study was to assess whether patients with severe radiation-induced sequelae (RIS; i.e., National Cancer Institute/CTCv3.0 grade, z3) display both a low capacity of radiation-induced CD8 lymphocyte apoptosis (RILA) in vitro and possess certain single nucleotide polymorphisms (SNP) located in candidate genes associated with the response of cells to radiation. Experimental Design: DNA was isolated from blood samples obtained from patients (n = 399) included in the Swiss prospective study evaluating the predictive effect of in vitro RILA and RIS. SNPs in the ATM, SOD2, XRCC1, XRCC3, TGFB1, and RAD21 genes were screened in patients who experienced severe RIS (group A, n = 16) and control subjects who did not manifest any evidence of RIS (group B, n = 18). Results: Overall, 13 and 21 patients were found to possess a total of <4 and z4 SNPs in the candidate genes. The median (range) RILA in group A was 9.4% (5.3-16.5) and 94% (95% confidence interval, 70-100) of the patients (15 of 16) had z4 SNPs. In group B, median (range) RILA was 25.7% (20.2-43.2) and 33% (95% confidence interval, 13-59) of patients (6 of 18) had z4 SNPs (P < 0.001). Conclusions:The results of this study suggest that patients with severe RIS possess 4 or more SNPs in candidate genes and low radiation-induced CD8 lymphocyte apoptosis in vitro.

Intestinal metabolism can limit oral bioavailability of drugs and increase the risk of drug interactions. It is therefore important to be able to predict and quantify it in drug discovery and early development. In recent years, a plethora of models—in vivo, in situ and in vitro—have been discussed in the literature. The primary objective of this review is to summarize the current knowledge in the quantitative prediction of gut-wall metabolism. As well as discussing the successes of current models for intestinal metabolism, the challenges in the establishment of good preclinical models are highlighted, including species differences in the isoforms; regional abundances and activities of drug metabolizing enzymes; the interplay of enzyme-transporter proteins; and lack of knowledge on enzyme abundances and availability of empirical scaling factors. Due to its broad specificity and high abundance in the intestine, CYP3A is the enzyme that is frequently implicated in human gut metabolism and is therefore the major focus of this review. A strategy to assess the impact of gut wall metabolism on oral bioavailability during drug discovery and early development phases is presented. Current gaps in the mechanistic understanding and the prediction of gut metabolism are highlighted, with suggestions on how they can be overcome in the future.

Gravimetry, the precise measurement of gravitational fields, can be used to probe the internal structure of Earth and other planets. The Curiosity rover on Mars carries accelerometers normally used for navigation and attitude determination. We have recalibrated them to isolate the signature of the changing gravitational acceleration as the rover climbs through Gale crater. The subsurface rock density is inferred from the measured decrease in gravitational field strength with elevation. The density of the sedimentary rocks in Gale crater is 1680 ± 180 kilograms per cubic meter. This value is lower than expected, indicating a high porosity and constraining maximum burial depths of the rocks over their history.