Real-world application of PBPK in drug discovery.

The utility of PBPK models in support of drug development has been well documented. During the discovery stage, PBPK has increasingly been applied for early risk assessment, prediction of human dose, toxicokinetic dose projection and early formulation assessment. Previous review articles have proposed model building and application strategies for PBPK-based first in human predictions with comprehensive descriptions of the individual components of PBPK models. This includes the generation of decision trees, based on comprehensive literature reviews, to guide the application of PBPK in the discovery setting. The goal of this mini review is to provide additional guidance on the real-world application of PBPK, in support of the discovery stage of drug development. In this mini review, our goal is to provide guidance on the typical steps involved in the development and application of a PBPK model during drug discovery to assist in decision making. We have illustrated our recommended approach through description of case examples, where PBPK has been successfully applied to aid in human PK projection, candidate selection and prediction of drug interaction liability for parent and metabolite. Through these case studies, we have highlighted fundamental issues, including pre-verification in preclinical species, the application of empirical scalars in the prediction of in vivo clearance from in vitro systems, in silico prediction of permeability and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. Significance Statement Through description of three case studies, we have highlighted the fundamental principles of PBPK application during drug discovery. These include pre-verification of the model in preclinical species, application of empirical scalars where necessary in the prediction of clearance, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed.

Physiologically Based Pharmacokinetic Modeling To Predict Drug-Biologic Interactions with Cytokine Modulators: Are These Relevant and Is Interleukin-6 Enough?

Drugs that modulate cytokine levels are often used for the treatment of cancer as well as inflammatory or immunologic disorders. Pharmacokinetic drug-biologic interactions (DBIs) may arise from suppression or elevation of cytochrome P450 (P450) enzymes caused by the increase or decrease in cytokine levels after administration of these therapies. There is in vitro and in vivo evidence that demonstrates a clear link between raised interleukin (IL)-6 levels and P450 suppression, in particular CYP3A4. However, despite this, the changes in IL-6 levels in vivo rarely lead to significant drug interactions (area under the curve and Cmax ratios < 2-fold). The clinical significance of such interactions therefore remains questionable and is dependent on the therapeutic index of the small molecule therapy. Physiologically based pharmacokinetic (PBPK) modeling has been used successfully to predict the impact of raised IL-6 on P450 activities. Beyond IL-6, published data show little evidence that IL-8, IL-10, and IL-17 suppress P450 enzymes. In vitro data suggest that IL-1ß, IL-2, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ can cause suppression of P450 enzymes. Despite in vivo there being a link between IL-6 levels and P450 suppression, the evidence to support a direct effect of IL-2, IL-8, IL-10, IL-17, IFN-γ, TNF-α, or vascular endothelial growth factor on P450 activity is inconclusive. This commentary will discuss the relevance of such drug-biologic interactions and whether current PBPK models considering only IL-6 are sufficient. SIGNIFICANCE STATEMENT: This commentary summarizes the current in vitro and in vivo literature regarding cytokine-mediated cytochrome P450 suppression and compares the relative suppressive potential of different cytokines in reference to interleukin (IL)-6. It also discusses the relevance of drug-biologic interactions to therapeutic use of small molecule drugs and whether current physiologically based pharmacokinetic models considering only IL-6 are sufficient to predict the extent of drug-biologic interactions.

Cure4Kids: Two decades of knowledge transfer.

Cure4Kids is a free web-based knowledge platform for professionals providing care for children with cancer and hematologic diseases, offering its users a comprehensive suite of learning opportunities. It has been a resource for the pediatric oncology community across the world for the past two decades, with 60,107 users having logged in 1,412,514 times with 22,045,553 content hits. A transformation of Cure4Kids is being planned and will include an improved user interface, increased interactivity, and more content.

Using facial muscular movements to understand young children's emotion regulation and concurrent neural activation.

Individual differences in young children's frustration responses set the stage for myriad developmental outcomes and represent an area of intense empirical interest. Emotion regulation is hypothesized to comprise the interplay of complex behaviors, such as facial expressions, and activation of concurrent underlying neural systems. At present, however, the literature has mostly examined children's observed emotion regulation behaviors and assumed underlying brain activation through separate investigations, resulting in theoretical gaps in our understanding of how children regulate emotion in vivo. Our goal was to elucidate links between young children's emotion regulation-related neural activation, facial muscular movements, and parent-rated temperamental emotion regulation. Sixty-five children (age 3-7) completed a frustration-inducing computer task while lateral prefrontal cortex (LPFC) activation and concurrent facial expressions were recorded. Negative facial expressions with eye constriction were inversely associated with both parent-rated temperamental emotion regulation and concurrent LPFC activation. Moreover, we found evidence that positive expressions with eye constriction during frustration may be associated with stronger LPFC activation. Results suggest a correspondence between facial expressions and LPFC activation that may explicate how children regulate emotion in real time.

Evaluation of the antitumor effects of c-Myc-Max heterodimerization inhibitor 100258-F4 in ovarian cancer cells.

Epithelial ovarian carcinoma is the most lethal gynecological cancer due to its silent onset and recurrence with resistance to chemotherapy. Overexpression of oncogene c-Myc is one of the most frequently encountered events present in ovarian carcinoma. Disrupting the function of c-Myc and its downstream target genes is a promising strategy for cancer therapy. Our objective was to evaluate the potential effects of small-molecule c-Myc inhibitor, 10058-F4, on ovarian carcinoma cells and the underlying mechanisms by which 10058-F4 exerts its actions. Using MTT assay, colony formation, flow cytometry and Annexin V FITC assays, we found that 10058-F4 significantly inhibited cell proliferation of both SKOV3 and Hey ovarian cancer cells in a dose dependent manner through induction of apoptosis and cell cycle G1 arrest. Treatment with 10058-F4 reduced cellular ATP production and ROS levels in SKOV3 and Hey cells. Consistently, primary cultures of ovarian cancer treated with 10058-F4 showed induction of caspase-3 activity and inhibition of cell proliferation in 15 of 18 cases. The response to 10058-F4 was independent the level of c-Myc protein over-expression in primary cultures of ovarian carcinoma. These novel findings suggest that the growth of ovarian cancer cells is dependent upon c-MYC activity and that targeting c-Myc-Max heterodimerization could be a potential therapeutic strategy for ovarian cancer.

Physiologically-based pharmacokinetic modeling of the drug-drug interaction between ivacaftor and lefamulin in cystic fibrosis patients.

Lefamulin is being evaluated as a treatment for bacterial exacerbations in cystic fibrosis (CF). Ivacaftor is approved for the treatment of patients with CF. Lefamulin is a moderate CYP3A inhibitor and co-administration with ivacaftor may result in a drug-drug interaction (DDI). A CF population was built based on literature using the Simcyp Simulator. A previously developed and validated physiologically-based pharmacokinetic (PBPK) model for ivacaftor was used. A PBPK model for lefamulin was developed and verified. Predicted concentrations and pharmacokinetic (PK) parameters for both ivacaftor and lefamulin in healthy subjects and patients with CF were in reasonable agreement with observed data (within 1.4-fold, majority within 1.25-fold). The lefamulin model as a CYP3A4 perpetrator was validated using a different Ki value for oral (p.o.) and intravenous (i.v.) routes. The simulated changes in area under the curve of ivacaftor in patients with CF when co-administered with p.o. and i.v. lefamulin were weak-to-moderate. The predicted change in ivacaftor PK when co-administered with oral lefamulin was less than observed between ivacaftor and fluconazole. These results suggest a low liability for a DDI between lefamulin and ivacaftor in patients with CF.

Determination of Vatiquinone Drug-Drug Interactions, as CYP450 Perpetrator and Victim, Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation.

Vatiquinone, a 15-lipoxygenase inhibitor, is in development for patients with Friedreich's ataxia. Physiologically based pharmacokinetic (PBPK) modeling addressed drug-drug interaction gaps without additional studies. A PBPK model (Simcyp Simulator version 21, full model) was developed using parameters obtained from in vitro studies, in silico estimation and optimization, and two clinical studies. A venous blood dosing model best characterized vatiquinone lymphatic absorption. Apparent oral clearance (CL/F) was used to optimize intrinsic clearance (CLint). Intestinal availability (Fg) was estimated using the hybrid flow term (Qgut), unbound fraction in the enterocytes (fugut), and gut intrinsic metabolic clearance (CLuG,int). Renal clearance (CLR) was set to zero. Assuming an Fa of 1, CYP3A4 contribution (fmCYP3A4) was further optimized. The PBPK model was verified with two clinical studies and demonstrated that it adequately characterized vatiquinone PK. As a perpetrator, the model predicted no risk for vatiquinone to significantly alter the drug exposures of CYP3A4 and CYP1A2 substrates as evident bynegligible reduction in both midazolam and caffeine area under the curve (AUC)inf and Cmax. As a victim, the model predicted that vatiquinone exposures are weakly influenced by moderate CYP3A4 inhibitors and inducers. With fluconazole coadministration, vatiquinone AUCinf and Cmax increased by nearly 50% and 25%, respectively. With efavirenz coadministration, vatiquinone AUCinf and Cmax decreased by approximately 20% and 10%, respectively. Results suggested that vatiquinone does not significantly impact CYP3A4 and CYP1A2 substrates and that moderate CYP3A4 inhibitors and inducers weakly impact vatiquinone AUC.

Creation of Novel Sensitive Probe Substrate and Moderate Inhibitor Models for a Comprehensive Prediction of CYP2C8 Interactions for Tucatinib.

A physiologically-based pharmacokinetic (PBPK) model was developed to simulate plasma concentrations of tucatinib (TUKYSA®) after single-dose or multiple-dose administration of 300 mg b.i.d. orally. This PBPK model was subsequently applied to support evaluation of drug-drug interaction (DDI) risk as a perpetrator resulting from tucatinib inhibition of CYP3A4, CYP2C8, CYP2C9, P-gp, or MATE1/2-K. The PBPK model was also applied to support evaluation of DDI risk as a victim resulting from co-administration with CYP3A4 or CYP2C8 inhibitors, or a CYP3A4 inducer. After refinement with clinical DDI data, the final PBPK model was able to recover the clinically observed single and multiple-dose plasma concentrations for tucatinib when tucatinib was administered as a single agent in healthy subjects. In addition, the final model was able to recover clinically observed plasma concentrations of tucatinib when administered in combination with itraconazole, rifampin, or gemfibrozil as well as clinically observed plasma concentrations of probe substrates of CYP3A4, CYP2C8, CYP2C9, P-gp, or MATE1/2-K. The PBPK model was then applied to prospectively predict the potential perpetrator or victim DDIs with other substrates, inducers, or inhibitors. To simulate a potential interaction with a moderate CYP2C8 inhibitor, two novel PBPK models representing a moderate CYP2C8 inhibitor and a sensitive CYP2C8 substrate were developed based on the existing PBPK models for gemfibrozil and rosiglitazone, respectively. The simulated population geometric mean area under the curve ratio of tucatinib with a moderate CYP2C8 inhibitor ranged from 1.98- to 3.08-fold, and based on these results, no dose modifications were proposed for moderate CYP2C8 inhibitors for the tucatinib label.

Discovery of electrophilic degraders that exploit SNAr chemistry.

Targeted covalent inhibition (TCI) and targeted protein degradation (TPD) have proven effective in pharmacologically addressing formerly 'undruggable' targets. Integration of both methodologies has resulted in the development of electrophilic degraders where recruitment of a suitable E3 ubiquitin ligase is achieved through formation of a covalent bond with a cysteine nucleophile. Expanding the scope of electrophilic degraders requires the development of electrophiles with tempered reactivity that enable selective ligase recruitment and reduce cross-reactivity with other cellular nucleophiles. In this study, we report the use of chemical moieties that enable nucleophilic aromatic substitution (SNAr) reactions in the rational design of electrophilic protein degraders. Appending an SNAr covalent warhead to several preexisting small molecule inhibitors transformed them into degraders, obviating the need for a defined E3 ligase recruiter. The SNAr covalent warhead is versatile; it can recruit various E3 ligases, including DDB1 and CUL4 associated factor 11 (DCAF11), DDB1 and CUL4 associated factor 16 (DCAF16), and possibly others. The incorporation of an SNAr covalent warhead into the BRD4 inhibitor led to the discovery of degraders with low picomolar degradation potency. Furthermore, we demonstrate the broad applicability of this approach through rational functional switching from kinase inhibitors into potent degraders.

Predicting pharmacokinetic profiles using in silico derived parameters.

Human pharmacokinetic (PK) predictions play a critical role in assessing the quality of potential clinical candidates where the accurate estimation of clearance, volume of distribution, bioavailability, and the plasma-concentration-time profiles are the desired end points. While many methods for conducting predictions utilize in vivo data, predictions can be conducted successfully from in vitro or in silico data, applying modeling and simulation techniques. This approach can be facilitated using commercially available prediction software such as GastroPlus which has been reported to accurately predict the oral PK profile of small drug-like molecules. Herein, case studies are described where GastroPlus modeling and simulation was employed using in silico or in vitro data to predict PK profiles in early discovery. The results obtained demonstrate the feasibility of adequately predicting plasma-concentration-time profiles with in silico derived as well as in vitro measured parameters and hence predicting PK profiles with minimal data. The applicability of this approach can provide key information enabling decisions on either dose selection, chemistry strategy to improve compounds, or clinical protocol design, thus demonstrating the value of modeling and simulation in both early discovery and exploratory development for predicting absorption and disposition profiles.

A mathematical model of the human menstrual cycle for the administration of GnRH analogues.

The paper presents a differential equation model for the feedback mechanisms between gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle. Compared to earlier human cycle models, there are three important differences: The model presented here (a) does not involve any delay equations, (b) is based on a deterministic modeling of the GnRH pulse pattern, and (c) contains less differential equations and less parameters. These differences allow for a faster simulation and parameter identification. The focus is on modeling GnRH-receptor binding, in particular, by inclusion of a pharmacokinetic/pharmacodynamic (PK/PD) model for a GnRH agonist, Nafarelin, and a GnRH antagonist, Cetrorelix, into the menstrual cycle model. The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle of 12 healthy women. It correctly predicts hormonal changes following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle.

A comprehensive evaluation in clinic and physiologically-based pharmacokinetic modeling and simulation to confirm lack of cytochrome P450-mediated drug-drug interaction potential for pomotrelvir.

Pomotrelvir is a new chemical entity and potent direct-acting antiviral inhibitor of the main protease of coronaviruses. Here the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of pomotrelvir was evaluated for major CYP isoforms, starting with in vitro assays followed by the basic static model assessment. The identified CYP3A4-mediated potential DDIs were evaluated clinically at a supratherapeutic dose of 1050 mg twice daily (b.i.d.) of pomotrelvir, including pomotrelvir coadministration with ritonavir (strong inhibitor of CYP3A4) or midazolam (sensitive substrate of CYP3A4). Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed within the Simcyp Population-based Simulator using in vitro and in vivo information and validated with available human pharmacokinetic (PK) data. The PBPK model was simulated to assess the DDI potential for CYP isoforms that pomotrelvir has shown a weak to moderate DDI in vitro and for CYP3A4 at the therapeutic dose of 700 mg b.i.d. To support the use of pomotrelvir in women of childbearing potential, the impact of pomotrelvir on the exposure of the representative oral hormonal contraceptive drugs ethinyl estradiol and levonorgestrel was assessed using the PBPK model. The overall assessment suggested weak inhibition of pomotrelvir on CYP3A4 and minimal impact of a strong CYP3A4 inducer or inhibitor on pomotrelvir PK. Therefore, pomotrelvir is not anticipated to have clinically meaningful DDIs at the clinical dose. These comprehensive in vitro, in clinic, and in silico efforts indicate that the DDI potential of pomotrelvir is minimal, so excluding patients on concomitant medicines in clinical studies would not be required.

Physiologically Based Pharmacokinetic Model Development and Verification for Bioequivalence Testing of Bempedoic Acid Oral Suspension and Reference Tablet Formulation.

The bioequivalence of bempedoic acid oral suspension and commercial immediate release (IR) tablet formulations were assessed using a physiologically based pharmacokinetic (PBPK) model. The mechanistic model, developed from clinical mass balance results and in vitro intrinsic solubility, permeability, and dissolution data, was verified against observed clinical pharmacokinetics (PK) results. Model inputs included a fraction of a dose in solution (0.01%), viscosity (118.8 cps), and median particle diameter (50 µm) for the suspension and particle diameter (36.4 µm) for IR tablets. Dissolution was determined in the relevant media (pH 1.2-6.8) in vitro. Model simulations of bioequivalence predicted oral suspension (test) to IR tablet (reference) geometric mean ratio estimates of 96.9% (90% confidence interval [CI]: 92.6-101) for maximum concentration and 98.2% (90% CI: 87.3-111) for the area under the concentration-time curve. Sensitivity analyses showed gastric transit time had a minor impact on model predictions. Oral suspension biopharmaceutical safe space was defined by extremes of particle size and the percent of bempedoic acid in solution. PBPK model simulations predicted that the rate and extent of bempedoic acid absorption are unlikely to exhibit clinically meaningful differences when dosed as an oral suspension compared with an IR tablet without requiring a clinical bioequivalence study in adults.

Tiered approach to evaluate the CYP3A victim and perpetrator drug-drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling.

Vonoprazan is metabolized extensively through CYP3A and is an in vitro time-dependent inhibitor of CYP3A. A tiered approach was applied to understand the CYP3A victim and perpetrator drug-drug interaction (DDI) potential for vonoprazan. Mechanistic static modeling suggested vonoprazan is a potential clinically relevant CYP3A inhibitor. Thus, a clinical study was conducted to evaluate the impact of vonoprazan on the exposure of oral midazolam, an index substrate for CYP3A. A physiologically-based pharmacokinetic (PBPK) model for vonoprazan was also developed using in vitro data, drug- and system-specific parameters, and clinical data and observations from a [14 C] human absorption, distribution, metabolism, and excretion study. The PBPK model was refined and verified using data from a clinical DDI study with the strong CYP3A inhibitor, clarithromycin, to confirm the fraction metabolized by CYP3A, and the oral midazolam clinical DDI data assessing vonoprazan as a time-dependent inhibitor of CYP3A. The verified PBPK model was applied to simulate the anticipated changes in vonoprazan exposure due to moderate and strong CYP3A inducers (efavirenz and rifampin, respectively). The clinical midazolam DDI study indicated weak inhibition of CYP3A, with a less than twofold increase in midazolam exposure. PBPK simulations projected a 50% to 80% reduction in vonoprazan exposure when administered concomitantly with moderate or strong CYP3A inducers. Based on these results, the vonoprazan label was revised and states that lower doses of sensitive CYP3A substrates with a narrow therapeutic index should be used when administered concomitantly with vonoprazan, and co-administration with moderate and strong CYP3A inducers should be avoided.

Development and application of a PBPK modeling strategy to support antimalarial drug development.

As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically-based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real-world development and clinical applications, including the assessment of the drug-drug interaction liability between combination partners or with co-administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.

Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data.

With efforts to reduce cytochrome P450-mediated clearance (CL) during the early stages of drug discovery, transporter-mediated CL mechanisms are becoming more prevalent. However, the prediction of plasma concentration-time profiles for such compounds using physiologically based pharmacokinetic (PBPK) modeling is far less established in comparison with that for compounds with passively mediated pharmacokinetics (PK). In this study, we have assessed the predictability of human PK for seven organic anion-transporting polypeptide (OATP) substrates (pravastatin, cerivastatin, bosentan, fluvastatin, rosuvastatin, valsartan, and repaglinide) for which clinical intravenous data were available. In vitro data generated from the sandwich culture human hepatocyte system were simultaneously fit to estimate parameters describing both uptake and biliary efflux. Use of scaled active uptake, passive distribution, and biliary efflux parameters as inputs into a PBPK model resulted in the overprediction of exposure for all seven drugs investigated, with the exception of pravastatin. Therefore, fitting of in vivo data for each individual drug in the dataset was performed to establish empirical scaling factors to accurately capture their plasma concentration-time profiles. Overall, active uptake and biliary efflux were under- and overpredicted, leading to average empirical scaling factors of 58 and 0.061, respectively; passive diffusion required no scaling factor. This study illustrates the mechanistic and model-driven application of in vitro uptake and efflux data for human PK prediction for OATP substrates. A particular advantage is the ability to capture the multiphasic plasma concentration-time profiles for such compounds using only preclinical data. A prediction strategy for novel OATP substrates is discussed.

In vitro evaluation of hepatic transporter-mediated clinical drug-drug interactions: hepatocyte model optimization and retrospective investigation.

To assess the feasibility of using sandwich-cultured human hepatocytes (SCHHs) as a model to characterize transport kinetics for in vivo pharmacokinetic prediction, the expression of organic anion-transporting polypeptide (OATP) proteins in SCHHs, along with biliary efflux transporters, was confirmed quantitatively by liquid chromatography-tandem mass spectrometry. Rifamycin SV (Rif SV), which was shown to completely block the function of OATP transporters, was selected as an inhibitor to assess the initial rates of active uptake. The optimized SCHH model was applied in a retrospective investigation of compounds with known clinically significant OATP-mediated uptake and was applied further to explore drug-drug interactions (DDIs). Greater than 50% inhibition of active uptake by Rif SV was found to be associated with clinically significant OATP-mediated DDIs. We propose that the in vitro active uptake value therefore could serve as a cutoff for class 3 and 4 compounds of the Biopharmaceutics Drug Disposition Classification System, which could be integrated into the International Transporter Consortium decision tree recommendations to trigger clinical evaluations for potential DDI risks. Furthermore, the kinetics of in vitro hepatobiliary transport obtained from SCHHs, along with protein expression scaling factors, offer an opportunity to predict complex in vivo processes using mathematical models, such as physiologically based pharmacokinetics models.

Use of modelling and simulation techniques to support decision making on the progression of PF-04878691, a TLR7 agonist being developed for hepatitis C.

AIM: To use non-linear mixed effects modelling and simulation techniques to predict whether PF-04878691, a toll-like receptor 7 (TLR7) agonist, would produce sufficient antiviral efficacy while maintaining an acceptable side effect profile in a 'proof of concept' (POC) study in chronic hepatitis C (HCV) patients. METHODS: A population pharmacokinetic-pharmacodynamic (PKPD) model was developed using available 'proof of pharmacology' (POP) clinical data to describe PF-04878691 pharmacokinetics (PK) and its relationship to 2',5'-oligoadenylate synthetase (OAS; marker of pharmacology) and lymphocyte levels (marker of safety) following multiple doses in healthy subjects. A second model was developed to describe the relationship between change from baseline OAS expressed as fold change and HCV viral RNA concentrations using clinical data available in HCV patients for a separate compound, CPG-10101 (ACTILON™), a TLR9 agonist. Using these models the antiviral efficacy and safety profiles of PF-04878691 were predicted in HCV patients. RESULTS: The population PKPD models described well the clinical data as assessed by visual inspection of diagnostic plots, visual predictive checks and precision of the parameter estimates. Using these relationships, PF-04878691 exposure and HCV viral RNA concentration was simulated in HCV patients receiving twice weekly administration for 4 weeks over a range of doses. The simulations indicated that significant reductions in HCV viral RNA concentrations would be expected at doses > 6 mg. However at these doses grade ≥ 3 lymphopenia was also predicted. CONCLUSIONS: The model simulations indicate that PF-04878691 is unlikely to achieve POC criteria and support the discontinuation of this compound for the treatment of HCV.

The evolution of the OATP hepatic uptake transport protein family in DMPK sciences: from obscure liver transporters to key determinants of hepatobiliary clearance.

Over the last two decades the impact on drug pharmacokinetics of the organic anion transporting polypeptides (OATPs: OATP-1B1, 1B3 and 2B1), expressed on the sinusoidal membrane of the hepatocyte, has been increasingly recognized. OATP-mediated uptake into the hepatocyte coupled with subsequent excretion into bile via efflux proteins, such as MRP2, is often referred to as hepatobiliary excretion. OATP transporter proteins can impact some drugs in several ways including pharmacokinetic variability, pharmacodynamic response and drug-drug interactions (DDIs). The impact of transporter mediated hepatic clearance is illustrated with case examples, from the literature and also from the Pfizer portfolio. The currently available in vitro techniques to study the hepatic transporter proteins involved in the hepatobiliary clearance of drugs are reviewed herein along with recent advances in using these in vitro data to predict the human clearance of compounds recognized by hepatic uptake transporters.

Application of PBPK modelling in drug discovery and development at Pfizer.

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.