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.

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development.

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.

The intestinal efflux transporter breast cancer resistance protein (BCRP) restricts the absorption of rosuvastatin. Of the transporters important to rosuvastatin disposition, fostamatinib inhibited BCRP (IC 50 = 50 nM) and organic anion-transporting polypeptide 1B1 (OATP1B1; IC 50 > 10 mM), but not organic anion transporter 3, in vitro, predicting a drug-drug interaction (DDI) in vivo through inhibition of BCRP only. Consequently, a clinical interaction study between fostamatinib and rosuvastatin was performed (and reported elsewhere). This confirmed the critical role BCRP plays in statin absorption, as inhibition by fostamatinib resulted in a significant 1.96-fold and 1.88-fold increase in rosuvastatin area under the plasma concentration-time curve (AUC) and C max , respectively. An in vitro BCRP inhibition assay, using polarized Caco-2 cells and rosuvastatin as probe substrate, was subsequently validated with literature inhibitors and used to determine BCRP inhibitory potencies (IC 50 ) of the perpetrator drugs eltrombopag, darunavir, lopinavir, clopidogrel, ezetimibe, fenofibrate, and fluconazole. OATP1B1 inhibition was also determined using human embryonic kidney 293-OATP1B1 cells versus estradiol 17b-glucuronide. Calculated parameters of maximum enterocyte concentration [I gut max ], maximum unbound hepatic inlet concentration, transporter fraction excreted value, and determined IC 50 value were incorporated into mechanistic static equations to compute theoretical increases in rosuvastatin AUC due to inhibition of BCRP and/or OATP1B1. Calculated theoretical increases in exposure correctly predicted the clinically observed changes in rosuvastatin exposure and suggested intestinal BCRP inhibition (not OATP1B1) to be the mechanism underlying the DDIs with these drugs. In conclusion, solitary inhibition of the intestinal BCRP transporter can result in clinically significant DDIs with rosuvastatin, causing up to a maximum 2-fold increase in exposure, which may warrant statin dose adjustment in clinical practice.

The tenets of biopharmaceutics, solubility and permeability, are of pivotal importance in new drug discovery and lead optimization due to the dependence of drug absorption and pharmacokinetics on these two properties. A classification system for drugs based on these two fundamental parameters, Biopharmaceutic Classification System (BCS), provides drug designer an opportunity to manipulate structure or physicochemical properties of lead candidates so as to achieve better "deliverability". Considering the facts for failure of NCEs, drug research, once concentrating on optimizing the efficacy and safety of the leads, dramatically transformed in the past two decades. With the enormous number of molecules being synthesized using combinatorial and parallel synthesis, high throughput methodologies for screening solubility and permeability has gained significant interest in pharmaceutical industry. Ultimate aim of the drug discovery scientist in pharmacokinetic optimization is to tailor the molecules so that they show the features of BCS class I without compromising on pharmacodynamics. Considerations to optimize drug delivery and pharmacokinetics right from the initial stages of drug design propelled need for "High Throughput Pharmaceutics" (HTP). In silico predictions and development of theoretical profiles for solubility and lipophilicity provides structure based biopharmaceutical optimization, while in vitro experimental models (microtitre plate assays and cell cultures) validate the predictions. Thus, biopharmaceutical characterization during drug design and early development helps in early withdrawal of molecules with insurmountable developmental problems associated with pharmacokinetic optimization.