Kunal S. Taskar

The study presents a comprehensive evaluation of physiologically-based pharmacokinetic (PBPK) modeling for Proteolysis Targeting Chimeras (PROTACs), using

Antisense oligonucleotide (ASO) therapeutics present new opportunities for treating challenging-to-treat diseases. Prediction of concentration–time course in systemic circulation and tissues within the context of large molecule physiologically based pharmacokinetic (PBPK) modelling has proven to be useful in animal-to-human extrapolation and first-in-human dose selection. A human PBPK model was developed and verified using bepirovirsen clinical data to predict plasma pharmacokinetics (PK) and tissue concentrations. Liver and kidney partition coefficient ratios from monkey studies, corrected for plasma unbound fraction in monkeys and humans, informed bepirovirsen concentration predictions in the human liver and kidney. Liver and kidney partition coefficients were calculated to be 2147 and 2822, respectively. All predicted PK parameters in healthy volunteers (except t _max ) were within two-folds of observed data. Predicted vs . observed clearance (L/h), AUC _0-inf (µg.h/mL) and C _max (µg/mL) for the 300 mg single dose in healthy volunteers were 2.94, 114.43, and 10.56 vs . 2.25, 136.6, and 6.5, respectively. The majority of observed plasma concentrations for all doses were within the 5th and 95th percentiles of the predictions. The evaluated model was used to predict the impact of moderate hepatic impairment on bepirovirsen PK in virtual patients. The predicted vs . observed bepirovirsen exposure in moderate hepatic impairment was 0.9 and 0.7-fold lower, respectively, compared with healthy volunteers. The PBPK model predicted the liver and kidney tissue C _max values to be 172 and 132 µg/mL, respectively. In conclusion, a PBPK modelling approach for bepirovirsen, an ASO, is presented in this article and offers opportunities for future applications to other oligonucleotide therapeutics. Graphical Abstract

A physiologically‐based pharmacokinetic (PBPK) model of niraparib and its primary metabolite using a relevant virtual cancer population is reported here. A series of in vitro experiments using liver S9, microsomes, and hepatocytes with various inhibitors and recombinant supersomes demonstrated that niraparib is specifically metabolized by carboxylesterase 1 via amide hydrolysis to an acid metabolite (M1). Available virtual cancer populations, along with reference populations, were applied to modeling simulations using fixed trial designs with demographic and clinical chemistry parameters from patients receiving niraparib in clinical studies. Simulations of niraparib and its metabolite M1 were verified across numerous available clinical studies and repeat dose ranges in cancer patients within 2‐fold. The PBPK model was used to simulate exposures in moderately hepatic impaired, healthy Chinese and Japanese virtual populations as a surrogate of cancer comorbidity. The PBPK model confirmed minimal DDI liability with niraparib as a precipitant for most in vitro tested drug metabolizing enzymes and transporters. In vitro, niraparib lacks any CYP inhibition, induces CYP1A2 but not CYP3A4, and is not a CYP substrate, unlike some other PARPi's, which inhibit and induce numerous enzymes/transporters and are objects of CYP metabolism. At clinically relevant doses of niraparib ≥ 200 mg, a weak induction risk is predicted with sensitive CYP1A2 substrates, such as caffeine, and both niraparib and olaparib clinically increase serum creatinine in cancer patients, with up to a moderate inhibition risk predicted with MATE‐1/‐2K substrates, such as metformin, using a PBPK model of niraparib in the absence of a dedicated DDI study.

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.

Physiology Based Pharmacokinetic (PBPK) modeling is an established essential tool for predicting and/or analyzing drug–drug interactions (DDI). Uncertainty and variability associated with in vitro determined DDI‐related parameters have often been considered a limitation for predicting PBPK‐DDIs. Sensitivity analysis (SA) around DDI input parameters using PBPK analysis is often applied for assessing the relevance of clinical DDI predictions/prioritization/study designs. This perspective aims to explore and advocate practical approaches for precipitant (inhibitor/inducer) PBPK‐DDI SA for optimal clinically relevant evaluations.

Background/Objective: Cabotegravir (CAB), available as an oral tablet and as a long-acting (LA) nanosuspension for intramuscular injection, is approved as a combination therapy for the treatment, and as a monotherapy for the prevention, of HIV-1 infection. People living with HIV may receive multiple concomitant medications, with the associated risk of drug–drug interactions (DDIs). CAB is an inhibitor of OAT1/OAT3 renal transporters and a substrate of the UDP-glucuronosyltransferase enzymes UGT1A1 and 1A9, in vitro. While the effect of induction of UGT1A1/UGT1A9 on CAB exposure had been investigated in the clinic, the effect of the risk of DDIs with CAB via inhibition of these enzymes, or as an inhibitor of OAT1/OAT3 transporters, had not been evaluated. Methods: A physiologically-based pharmacokinetic (PBPK) model was developed and verified for orally dosed CAB to investigate the DDI risks associated with CAB, using a matrix approach to extensively qualify the PBPK platform and the substrates and/or inhibitors of either OAT1/OAT3 or UGT1A1/UGT1A9. The effect of uncertainties in in vitro inhibition values for OAT1/OAT3 was assessed via sensitivity analysis. Results: A mean increase of less than 25% in systemic exposure for OAT1/OAT3 substrates was predicted, with the potential for an increase of up to 80% based on the sensitivity analysis. On co-dosing with UGT1A1/UGT1A9 inhibitors, the predicted mean increase in CAB exposure was within 11%. Conclusions: PBPK modelling indicated that clinically relevant DDIs are not anticipated with OAT1/3 substrates or UGT1A1/1A9 inhibitors and CAB. With maximal exposure of the LA formulation of CAB being lower than the oral, the results of these simulations can be extrapolated to LA injectable dosing.

The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) model for conversion of phosphate prodrugs to active drug via intestinal alkaline phosphatase (IAP) implementing a generalized modeling strategy. Fostemsavir and fostamatinib were chosen as model drugs since there is extensive clinical pharmacokinetic data following administration of oral formulations. LUA scripting was used to develop an "in vitro" to "in vivo" extrapolation of the conversion rate of prodrugs derived from Caco2 cell lines using an absolute IAP abundance approach. The Simcyp v23 platform was modified to generate a virtual population to reflect gastric emptying rates following administration of a moderate fat meal. The PBPK model predicted the results of three different extended-release (ER) tablets of fostemsavir under fasted and fed conditions as well as for powder in capsule and tablet immediate release (IR) formulations of fostamatinib. Retrospectively, the model was also able to assess the clinical relevance of the in vitro dissolution method to rate changes of different microcrystalline cellulose-based IR tablets of fostamatinib, observed in acidic media. All predictions were within 2-fold of the observed Cmax, AUC, and Tmax, with 81% being within 1.25-fold. The developed modeling strategy can be effectively adopted to increase the confidence of using PBPK modeling to prospectively assess the in vivo performance of phosphate prodrugs and support the development of optimal oral extended-release formulations for this class of drugs.

This study was designed to assess the quantitative performance of endogenous drug–drug interaction (DDI) biomarkers (N1‐methylnicotinamide (1‐NMN), N1‐methyladenosine (m¹A), and creatinine) for the organic cation transporters, OCT2 and MATE1/2K in the kidney. Ten healthy volunteers received cimetidine (400 and 800 mg, single dose) or dolutegravir (50 mg, twice a day) together with metformin (500 mg). Cimetidine and dolutegravir were considered to act mainly as MATE1/2K and OCT2 inhibitors, respectively. The renal clearance (CL_r) of metformin was decreased by 15.5% and 42.5% by cimetidine 400 and 800 mg, and by 26.8% and 56.9% by dolutegravir first and fifth doses, respectively. CL_r ratio (CL_rR) of 1‐NMN were 0.93 and 0.64 for cimetidine 400 and 800 mg, and 0.87 and 0.47 for dolutegravir first and fifth doses, respectively. CL_rR of m¹A was less than that of 1‐NMN: 1.0 and 0.80 for cimetidine 400 and 800 mg, and 0.77 and 0.71 for dolutegravir first and fifth doses, respectively. CL_r of creatinine was significantly decreased only by cimetidine 800 mg. Individual CL_rR of 1‐NMN and m¹A showed a positive correlation with the corresponding CL_rR of metformin with r² of 0.58 and 0.55, respectively. When evaluated individually, m¹A showed a better correlation during cimetidine periods (r² 0.64) than 1‐NMN (r² 0.36), but vice versa during dolutegravir periods (r² 1‐NMN, 0.80; m¹A, 0.32). These results suggest that 1‐NMN and m¹A might be more promising than creatinine as endogenous biomarkers for quantitatively assessing the DDI potential of investigational drugs for OCT2 and MATE1/2K based on their CL_rR change.

Physiologically‐based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug–drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well‐characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, f_m,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of f_m,CYP3A4 (0.086–1.0), Fg (0.11–1.0) and hepatic availability (0.09–0.96) were included. Predictions were most often accurate for compounds that are not P‐gp substrates or that are P‐gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first‐order absorption model might be more appropriate for a P‐gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P‐gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.