Application of PBPK modelling in drug discovery and development at Pfizer

Abstract

Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

Ontogeny of plasma proteins, albumin and binding of diazepam, cyclosporine, and deltamethrin

“…PBPK models have been widely utilized for three decades to assess the toxicity of chemical agents (35,36). Such models are finding increasing use in drug development and regulation (37)(38)(39). Very few models, however, have yet been adapted and utilized to forecast the systemic uptake and disposition of therapeutic agents in infants and children.…”

Investigating the Enteroenteric Recirculation of Apixaban, a Factor Xa Inhibitor: Administration of Activated Charcoal to Bile Duct-Cannulated Rats and Dogs Receiving an Intravenous Dose and Use of Drug Transporter Knockout Rats

“…IE has not been widely applied to understand dispositional profiles of drugs although studies have been reported to assess IE of drugs that are substrates of P-gp or BCRP using in vitro permeability assays, ex vivo segmental intestine excretion, and activated charcoal techniques (Mayer et al, 1996;Sparreboom et al, 1997;Gramatte and Oertel, 1999). With increasing use of physiologically based pharmacokinetic modeling to predict clinical profiles and drug-drug interaction potential that require accurate inputs of drug-related as well as physiological parameters (Bolger et al, 2009;Abuasal et al, 2012;Jones et al, 2012), full characterization of elimination pathways including IE, RTR, and EER is becoming increasingly important. The study clearly demonstrated that 1) IC represents 20-50% of the systemic clearance of apixaban; 2) the overall disposition of apixaban in rats and dogs was governed by a complex and dynamic interplay between absorption, intestinal/ renal excretion, and reabsorption in EER and RTR processes; and 3) species-dependent urinary excretion is likely due to different expression of renal efflux transporters.…”

Dose Selection Based on Physiologically Based Pharmacokinetic (PBPK) Approaches

“…Some in-house examples of where Pfizer have used PBPK approaches towards informed and model-based dose selection have been described in detail previously (88). These examples are described briefly below: (1) use of a high throughput dose optimisation PBPK tool in very early project stages to predict human dose for large number of compounds using in vitro or in silico inputs; (2) use of PBPK simulation techniques to help select the best compounds, doses and formulations to take forward to preclinical toxicological studies; (3) use of PBPK modelling at candidate selection to compare in-house and competitor substrate in terms of human PK and dose projection; (4) human PK and dose projections to help design first-in-human clinical trials; (5) food effect simulations to predict dose adjustments in the presence and absence of food; and (6) prediction of human PK mediated by polymorphic cytochrome P450 enzymes to estimate dose adjustments in these populations.…”

“…In later drug development stages, the model can be updated with animal data specific to the therapeutic mAb to enable translation from preclinical species to human populations. We will illustrate fit- Prospective prediction of first-in-human PK in healthy volunteers (72)(73)(74) Retrospective prediction of first-in-human PK in healthy volunteers (8-11, 16,71) Prediction of PK in paediatric populations (78)(79)(80)(81)(82) Prediction of PK in patients with hepatic impairment/ liver cirrhosis (75,76) Prediction of PK in patients with renal impairment (77,86,87) Predictions of PK in other patient populations (83)(84)(85) Predictions of PK in polymorphic populations (88) for-purpose PBPK models using one example from literature and one in-house example. Adalimumab is an anti-TNF-alpha mAb approved for the treatment of rheumatoid arthritis (RA), an autoimmune disease which leads to inflammation of joints.…”