Physiologically Based Pharmacokinetic modelling of drugs in pregnancy: A mini-review on availability and limitations.

Physiologically based pharmacokinetic (PBPK) modelling in pregnancy is a relatively new approach that is increasingly being used to assess drug systemic exposure in pregnant women to potentially inform dosing adjustments. Physiological changes throughout pregnancy are incorporated into mathematical models to simulate drug disposition in the maternal and fetal compartments as well as the transfer of drugs across the placenta. This mini-review gathers currently available pregnancy PBPK models for drugs commonly used during pregnancy. In addition, information about the main PBPK modelling platforms used, metabolism pathways, drug transporters, data availability and drug labels were collected. The aim of this mini-review is to provide a concise overview, demonstrate trends in the field, highlight understudied areas and identify current gaps of PBPK modelling in pregnancy. Possible future applications of this PBPK approach are discussed from a clinical, regulatory and industry perspective.

Safety, tolerability, and pharmacokinetics of half-life extended SARS-CoV-2-neutralizing monoclonal antibodies AZD7442 (tixagevimab/cilgavimab) in healthy Japanese adults.

INTRODUCTION: The aim of this study was to evaluate the safety, tolerability, pharmacokinetics, and pharmacodynamics of AZD7442 (tixagevimab/cilgavimab) in healthy Japanese adults. METHODS: In this randomized, double-blind, placebo-controlled, phase 1 study, AZD7442 was administered intramuscularly (300 or 600 mg) or intravenously (300 or 1000 mg) to healthy Japanese adults. Primary endpoints were safety, tolerability, and pharmacokinetics. Anti-drug antibodies and neutralizing antibody activities were secondary endpoints. RESULTS: A total of 40 participants were randomized to receive AZD7442 (n = 30) or placebo (n = 10). Adverse events (AEs) occurred in 12 (40%) and 3 (30%) participants, respectively; there were no deaths, serious AEs, or AEs leading to study withdrawal. Tixagevimab and cilgavimab had mean half-lives of 82.1-95.9 and 77.9-92.0 days, respectively, which were generally similar regardless of administration route. SARS-CoV-2-neutralizing antibody titers were >4-fold higher than baseline levels from Day 8 to Day 211 in participants receiving AZD7442. CONCLUSIONS: AZD7442 was well tolerated in healthy Japanese adults, with predictable pharmacokinetics and an extended half-life, consistent with previous studies. CLINICALTRIALS: gov, NCT04896541.

Interlaboratory Variability in the Madin-Darby Canine Kidney Cell Proteome.

Madin-Darby canine kidney (MDCK) cells are widely used to study epithelial cell functionality. Their low endogenous drug transporter protein levels make them an amenable system to investigate transepithelial permeation and drug transporter protein activity after their transfection. MDCK cells display diverse phenotypic traits, and as such, laboratory-to-laboratory variability in drug permeability assessments is observed. Consequently, in vitro-in vivo extrapolation (IVIVE) approaches using permeability and/or transporter activity data require calibration. A comprehensive proteomic quantification of 11 filter-grown parental or mock-transfected MDCK monolayers from 8 different pharmaceutical laboratories using the total protein approach (TPA) is provided. The TPA enables estimations of key morphometric parameters such as monolayer cellularity and volume. Overall, metabolic liability to xenobiotics is likely to be limited for MDCK cells due to the low expression of required enzymes. SLC16A1 (MCT1) was the highest abundant SLC transporter linked to xenobiotic activity, while ABCC4 (MRP4) was the highest abundant ABC transporter. Our data supports existing findings that claudin-2 levels may be linked to tight junction modulation, thus impacting trans-epithelial resistance. This unique database provides data on more than 8000 protein copy numbers and concentrations, thus allowing an in-depth appraisal of the control monolayers used in each laboratory.

Safety, Tolerability and Pharmacokinetics of Half-Life Extended Severe Acute Respiratory Syndrome Coronavirus 2 Neutralizing Monoclonal Antibodies AZD7442 (Tixagevimab-Cilgavimab) in Healthy Adults.

BACKGROUND: AZD7442 is a combination of extended half-life, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific neutralizing monoclonal antibodies (tixagevimab and cilgavimab). METHODS: This phase 1, first-in-human, randomized, double-blind, placebo-controlled, dose-escalation study evaluated AZD7442 administered intramuscularly (300 mg) or intravenously (300, 1000, or 3000 mg) in healthy adults (aged 18-55 years). The primary end point was safety and tolerability. Secondary end points included pharmacokinetics and antidrug antibodies. RESULTS: Between 18 August and 16 October 2020, a total of 60 participants were enrolled; 50 received AZD7442, and 10 received placebo. Adverse events (all of mild or moderate intensity) occurred in 26 participants (52.0%) in the AZD7442 groups and 8 (80.0%) in the placebo group. No infusion or injection site or hypersensitivity reactions occurred. Tixagevimab and cilgavimab had mean half-lives of approximately 90 days (range, 87.0-95.3 days for tixagevimab and 79.8--91.1 days for cilgavimab) and similar pharmacokinetic profiles over the 361-day study period. SARS-CoV-2-specific neutralizing antibody titers provided by AZD7442 were maintained above those in plasma from convalescent patients with coronavirus disease 2019 (COVID-19). CONCLUSIONS: AZD7442 was well tolerated in healthy adults, showing a favorable safety profile across all doses. Depending on the SARS-CoV-2 variant, pharmacokinetic analyses suggest the AZD7442 could offer protection for ≥6 months against symptomatic COVID-19 after a single 300-mg intramuscular administration. CLINICAL TRIALS REGISTRATION: NCT04507256.

Antibodies are proteins produced by the body in response to infections caused by microbes, including viruses. AZD7442 is a combination of 2 human antibodies, with an extended duration of effect, sourced from people who had recovered from coronavirus disease 2019 (COVID-19). These antibodies recognize a specific part (spike protein) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, and prevent the virus from infecting cells in the body. The current study evaluated the safety of AZD7442 in healthy volunteers. Sixty adults were given AZD7442 or placebo (salt solution) as injections into the muscle (300-mg dose) or infusions into a vein (300­3000-mg doses). The study did not find any safety issues with AZD7442, including at the highest dose. AZD7442 was measured in the blood 12 months after dosing, suggesting a long duration of protection. Following this study, AZD7442 was tested in larger clinical trials to investigate its potential in preventing and treating COVID-19. AZD7442 is currently authorized as treatment for outpatients with COVID-19 and as a preventive drug in people who may not respond well to COVID-19 vaccines and need additional protection (eg, those taking medications that dampen the immune system).

Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions.

Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression-based supervised machine-learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross-validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression-based machine-learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug-drug interaction risk assessment for new drug candidates.

Pharmacokinetics under the COVID-19 storm.

AIMS: The storm-like nature of the health crises caused by COVID-19 has led to unconventional clinical trial practices such as the relaxation of exclusion criteria. The question remains: how can we conduct diverse trials without exposing subgroups of populations to potentially harmful drug exposure levels? The aim of this study was to build a knowledge base of the effect of intrinsic/extrinsic factors on the disposition of several repurposed COVID-19 drugs. METHODS: Physiologically based pharmacokinetic (PBPK) models were used to study the change in the pharmacokinetics (PK) of drugs repurposed for COVID-19 in geriatric patients, different race groups, organ impairment and drug-drug interactions (DDIs) risks. These models were also used to predict epithelial lining fluid (ELF) exposure, which is relevant for COVID-19 patients under elevated cytokine levels. RESULTS: The simulated PK profiles suggest no dose adjustments are required based on age and race for COVID-19 drugs, but dose adjustments may be warranted for COVID-19 patients also exhibiting hepatic/renal impairment. PBPK model simulations suggest ELF exposure to attain a target concentration was adequate for most drugs, except for hydroxychloroquine, azithromycin, atazanavir and lopinavir/ritonavir. CONCLUSION: We demonstrate that systematically collated data on absorption, distribution, metabolism and excretion, human PK parameters, DDIs and organ impairment can be used to verify simulated plasma and lung tissue exposure for drugs repurposed for COVID-19, justifying broader patient recruitment criteria. In addition, the PBPK model developed was used to study the effect of age and ethnicity on the PK of repurposed drugs, and to assess the correlation between lung exposure and relevant potency values from in vitro studies for SARS-CoV-2.

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.

The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system-specific parameters. Machine learning has the potential to be utilized for the prediction of drug-drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine-learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalizability by learning from multicase historical data, and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically-based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable, and require a smaller sample size of data, although insights differ on a case-by-case basis. Therefore, they may be appropriate for later stages of drug-drug interaction assessment when more in vivo and clinical data are available. A combined approach of using mechanistic models to highlight features that can be used for training machine-learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations, and compare machine learning to mechanistic modeling for drug-drug interaction risk assessment across the stages of drug discovery and development.

Comparative Pharmacokinetics of Tixagevimab/Cilgavimab (AZD7442) Administered Intravenously Versus Intramuscularly in Symptomatic SARS-CoV-2 Infection.

AZD7442 (Evusheld) is a combination of two human anti-severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) monoclonal antibodies (mAbs), tixagevimab (AZD8895) and cilgavimab (AZD1061). Route of administration is an important consideration to improve treatment access. We assessed pharmacokinetics (PKs) of AZD7442 absorption following 600 mg administered intramuscularly (i.m.) in the thigh compared with 300 mg intravenously (i.v.) in ambulatory adults with symptomatic COVID-19. PK analysis included 84 of 110 participants randomized to receive i.m. AZD7442 and 16 of 61 randomized to receive i.v. AZD7442. Serum was collected prior to AZD7442 administration and at 24 hours and 3, 7, and 14 days later. PK parameters were calculated using noncompartmental methods. Following 600 mg i.m., the geometric mean maximum concentration (Cmax ) was 38.19 µg/mL (range: 17.30-60.80) and 37.33 µg/mL (range: 14.90-58.90) for tixagevimab and cilgavimab, respectively. Median observed time to maximum concentration (Tmax ) was 7.1 and 7.0 days for tixagevimab and cilgavimab, respectively. Serum concentrations after i.m. dosing were similar to the i.v. dose (27-29 µg/mL each component) at 3 days. The area under the concentration-time curve (AUC)0-7d geometric mean ratio was 0.9 for i.m. vs. i.v. Participants with higher weight or body mass index were more likely to have lower concentrations with either route. Women appeared to have higher interparticipant variability in concentrations compared with men. The concentrations of tixagevimab and cilgavimab after administration i.m. to the thigh were similar to those achieved with i.v. after 3 days from dosing. Exposure in the i.m. group was 90% of i.v. over 7 days. Administration to the thigh can be considered to provide consistent mAb exposure and improve access.

Harnessing Clinical Trial and Real-World Data Towards an Understanding of Sex Effects on Drug Pharmacokinetics, Pharmacodynamics and Efficacy.

Increasing clinical data on sex-related differences in drug efficacy and toxicity has highlighted the importance of understanding the impact of sex on drug pharmacokinetics and pharmacodynamics. Intrinsic differences between males and females, such as different CYP enzyme activity, drug transporter expression or levels of sex hormones can all contribute to different responses to medications. However, most studies do not include sex-specific investigations, leading to lack of sex-disaggregated pharmacokinetic and pharmacodynamic data. Based available literature, the potential influence of sex on exposure-response relationship has not been fully explored for many drugs used in clinical practice, though population-based pharmacokinetic/pharmacodynamic modelling is well-placed to explore this effect. The aim of this review is to highlight existing knowledge gaps regarding the effect of sex on clinical outcomes, thereby proposing future research direction for the drugs with significant sex differences. Based on evaluated drugs encompassing all therapeutic areas, 25 drugs demonstrated a clinically meaningful sex differences in drug exposure (characterised by ≥ 50% change in drug exposure) and this altered PK was correlated with differential response.

Development of and insights from systems pharmacology models of antibody-drug conjugates.

Antibody-drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well-established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC-specific mechanisms and use data-driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.

The SARS-CoV-2 monoclonal antibody combination, AZD7442, is protective in nonhuman primates and has an extended half-life in humans.

Despite the success of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines, there remains a need for more prevention and treatment options for individuals remaining at risk of coronavirus disease 2019 (COVID-19). Monoclonal antibodies (mAbs) against the viral spike protein have potential to both prevent and treat COVID-19 and reduce the risk of severe disease and death. Here, we describe AZD7442, a combination of two mAbs, AZD8895 (tixagevimab) and AZD1061 (cilgavimab), that simultaneously bind to distinct, nonoverlapping epitopes on the spike protein receptor binding domain to neutralize SARS-CoV-2. Initially isolated from individuals with prior SARS-CoV-2 infection, the two mAbs were designed to extend their half-lives and reduce effector functions. The AZD7442 mAbs individually prevent the spike protein from binding to angiotensin-converting enzyme 2 receptor, blocking virus cell entry, and neutralize all tested SARS-CoV-2 variants of concern. In a nonhuman primate model of SARS-CoV-2 infection, prophylactic AZD7442 administration prevented infection, whereas therapeutic administration accelerated virus clearance from the lung. In an ongoing phase 1 study in healthy participants (NCT04507256), a 300-mg intramuscular injection of AZD7442 provided SARS-CoV-2 serum geometric mean neutralizing titers greater than 10-fold above those of convalescent serum for at least 3 months, which remained threefold above those of convalescent serum at 9 months after AZD7442 administration. About 1 to 2% of serum AZD7442 was detected in nasal mucosa, a site of SARS-CoV-2 infection. Extrapolation of the time course of serum AZD7442 concentration suggests AZD7442 may provide up to 12 months of protection and benefit individuals at high-risk of COVID-19.

Current Practices, Gap Analysis, and Proposed Workflows for PBPK Modeling of Cytochrome P450 Induction: An Industry Perspective.

The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed.

Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P-glycoprotein-cytochrome P450 3A4 dual substrates.

Rifampicin induces both P-glycoprotein (P-gp) and cytochrome P450 3A4 (CYP3A4) through regulating common nuclear receptors (e.g., pregnane X receptor). The interplay of P-gp and CYP3A4 has emerged to be an important factor in clinical drug-drug interactions (DDIs) with P-gp-CYP3A4 dual substrates and requires qualitative and quantitative understanding. Although physiologically based pharmacokinetic (PBPK) modeling has become a widely accepted approach to assess DDIs and is able to reasonably predict DDIs caused by CYP3A4 induction and P-gp induction individually, the predictability of PBPK models for the effect of simultaneous P-gp and CYP3A4 induction on P-gp-CYP3A4 dual substrates remains to be systematically evaluated. In this study, we used a PBPK modeling approach for the assessment of DDIs between rifampicin and 12 drugs: three sensitive P-gp substrates, seven P-gp-CYP3A4 dual substrates, and two P-gp-CYP3A4 dual substrates and inhibitors. A 3.5-fold increase of intestinal P-gp abundance was incorporated in the PBPK models to account for rifampicin-mediated P-gp induction at steady state. The simulation results showed that accounting for P-gp induction in addition to CYP3A4 induction improved the prediction accuracy of the area under the concentration-time curve and maximum (peak) plasma drug concentration ratios compared with considering CYP3A4 induction alone. Furthermore, the interplay of relevant drug-specific parameters and its impact on the magnitude of DDIs were evaluated using sensitivity analysis. The PBPK approach described herein, in conjunction with robust in vitro and clinical data, can help in the prospective assessment of DDIs involving other P-gp and CYP3A4 dual substrates. The database reported in the present study provides a valuable aid in understanding the combined effect of P-gp and CYP3A4 induction during drug development.

ALT neuroblastoma chemoresistance due to telomere dysfunction-induced ATM activation is reversible with ATM inhibitor AZD0156.

Cancers overcome replicative immortality by activating either telomerase or an alternative lengthening of telomeres (ALT) mechanism. ALT occurs in ~25% of high-risk neuroblastomas, and progression in patients with ALT neuroblastoma during or after front-line therapy is frequent and often fatal. Temozolomide + irinotecan is commonly used as salvage therapy for neuroblastoma. Patient-derived cell lines and xenografts established from patients with relapsed ALT neuroblastoma demonstrated de novo resistance to temozolomide + irinotecan [SN-38 in vitro, P < 0.05; in vivo mouse event-free survival (EFS), P < 0.0001] vs. telomerase-positive neuroblastomas. We observed that ALT neuroblastoma cells manifested constitutive ataxia-telangiectasia mutated (ATM) activation due to spontaneous telomere dysfunction which was not observed in telomerase-positive neuroblastoma cells. We demonstrated that induction of telomere dysfunction resulted in ATM activation that, in turn, conferred resistance to temozolomide + SN-38 (4.2-fold change in IC50, P < 0.001). ATM knockdown (shRNA) or inhibition using a clinical-stage small-molecule inhibitor (AZD0156) reversed resistance to temozolomide + irinotecan in ALT neuroblastoma cell lines in vitro (P < 0.001) and in four ALT xenografts in vivo (EFS, P < 0.0001). AZD0156 showed modest to no enhancement of temozolomide + irinotecan activity in telomerase-positive neuroblastoma cell lines and xenografts. Ataxia telangiectasia and Rad3 related (ATR) inhibition using AZD6738 did not enhance temozolomide + SN-38 activity in ALT neuroblastoma cells. Thus, ALT neuroblastoma chemotherapy resistance occurs via ATM activation and is reversible with ATM inhibitor AZD0156. Combining AZD0156 with temozolomide + irinotecan warrants clinical testing for neuroblastoma.

Tumour-on-chip microfluidic platform for assessment of drug pharmacokinetics and treatment response.

Microphysiological in vitro systems are platforms for preclinical evaluation of drug effects and significant advances have been made in recent years. However, existing microfluidic devices are not yet able to deliver compounds to cell models in a way that reproduces the real physiological drug exposure. Here, we introduce a novel tumour-on-chip microfluidic system that mimics the pharmacokinetic profile of compounds on 3D tumour spheroids to evaluate their response to the treatments. We used this platform to test the response of SW620 colorectal cancer spheroids to irinotecan (SN38) alone and in combination with the ATM inhibitor AZD0156, using concentrations mimicking mouse plasma exposure profiles of both agents. We explored spheroid volume and viability as a measure of cancer cells response and changes in mechanistically relevant pharmacodynamic biomarkers (γH2AX, cleaved-caspase 3 and Ki67). We demonstrate here that our microfluidic tumour-on-chip platform can successfully predict the efficacy from in vivo studies and therefore represents an innovative tool to guide drug dose and schedules for optimal efficacy and pharmacodynamic assessment, while reducing the need for animal studies.

Mechanistic physiology-based pharmacokinetic modeling to elucidate vincristine-induced peripheral neuropathy following treatment with novel kinase inhibitors.

PURPOSE: Limited information is available regarding the drug-drug interaction (DDI) potential of molecular targeted agents and rituximab plus cyclophosphamide, doxorubicin (hydroxydaunorubicin), vincristine (Oncovin), and prednisone (R-CHOP) therapy. The addition of the Bruton tyrosine kinase (BTK) inhibitor ibrutinib to R-CHOP therapy results in increased toxicity versus R-CHOP alone, including higher incidence of peripheral neuropathy. Vincristine is a substrate of P-glycoprotein (P-gp, ABCB1); drugs that inhibit P-gp could potentially cause increased toxicity when co-administered with vincristine through DDI. While the combination of the BTK inhibitor acalabrutinib and R-CHOP is being explored clinically, the DDI potential between these therapies is unknown. METHODS: A human mechanistic physiology-based pharmacokinetic (PBPK) model of vincristine following intravenous dosing was developed to predict potential DDI interactions with combination therapy. In vitro absorption, distribution, metabolism, and excretion and in vivo clinical PK parameters informed PBPK model development, which was verified by comparing simulated vincristine concentrations with observed clinical data. RESULTS: While simulations suggested no DDI between vincristine and ibrutinib or acalabrutinib in plasma, simulated vincristine exposure in muscle tissue was increased in the presence of ibrutinib but not acalabrutinib. Extrapolation of the vincristine mechanistic PBPK model to other P-gp substrates further suggested DDI risk when ibrutinib (area under the concentration-time curve [AUC] ratio: 1.8), but not acalabrutinib (AUC ratio: 0.92), was given orally with venetoclax or digoxin. CONCLUSION: Overall, these data suggest low DDI risk between acalabrutinib and P-gp substrates with negligible increase in the potential risk of vincristine-induced peripheral neuropathy when acalabrutinib is added to R-CHOP therapy.

Blood retinal barrier and ocular pharmacokinetics: Considerations for the development of oncology drugs.

Tyrosine kinase inhibitors (TKIs) are an example of targeted drug therapy to treat cancer while minimizing damage to healthy tissue. In contrast to traditional oncology drugs, the toxicity profile of targeted therapies is less well understood and can include severe ocular adverse events, which are among the most common toxicity reported by these therapeutics. Inhibition of Mer receptor tyrosine kinase (MERTK) promotes innate tumor immunity by decreasing M2-macrophage polarization and efferocytosis. This mechanism offers the opportunity for targeted immunotherapy to treat cancer; however, the ocular expression of MERTK increases the difficulty for developing a targeted drug due to toxicity concerns. In this article we review the pharmacokinetic (PK) parameters and in vitro absorption, distribution, metabolism, and excretion (ADME) assays available to evaluate ocular disposition and assess the relationship between clinical PK and reported ocular events for TKIs to allow backtranslation to preclinical models. Understanding the ocular disposition in the context of PK and safety remains an evolving area and is likely to be a key aspect of developing safe and efficacious oncology drugs, devoid of ocular toxicity.

Food constituent- and herb-drug interactions in oncology: Influence of quantitative modelling on Drug labelling.

AIMS: Herbal products, spices and/or fruits are perceived as inherently healthy; for instance, St. John's wort (SJW) is marketed as a natural antidepressant and patients often self-administer it concomitantly with oncology medications. However, food constituents/herbs can interfere with drug pharmacokinetics, with risk of altering pharmacodynamics and efficacy. The objective of this work was to develop a strategy to prioritize herb- or food constituent-drug interactions (FC-DIs) to better assess oncology drug clinical risk. METHODS: Physiologically based pharmacokinetic (PBPK) models were developed by integrating in vitro parameters with the clinical pharmacokinetics of food constituents in grapefruit juice (bergamottin), turmeric (curcumin) or SJW (hyperforin). Perpetrator files were linked to verified victim PBPK models through appropriate interaction mechanisms (cytochrome P450 3A, breast cancer resistance protein, P-glycoprotein) and applied in prospective PBPK simulations to inform the likelihood and magnitude of changes in exposure to osimertinib, olaparib or acalabrutinib. RESULTS: Reported FC-DIs with oncology drugs were well recovered, with absolute average fold error values of 1.10 (bergamottin), 1.05 (curcumin) and 1.01 (hyperforin). Prospective simulations with grapefruit juice and turmeric showed clinically minor to insignificant changes in exposure (<1.50-fold) to acalabrutinib, osimertinib and olaparib, but predicted 1.57-fold FC-DI risk between acalabrutinib and curcumin. Moderate DDI risk was expected when acalabrutinib, osimertinib or olaparib were dosed with SJW. CONCLUSIONS: A model-informed decision tree based on mechanistic understanding of transporter and/or enzyme-mediated FC-DI is proposed based on bergamottin, curcumin and hyperforin FC-DI clinical data. Adopting this quantitative modelling approach should streamline herbal product safety assessments, assist in FC-DI management, and ultimately promote safe clinical use of oncology drugs.

Model-Informed Pediatric Dose Selection for Dapagliflozin by Incorporating Developmental Changes.

This analysis reports a quantitative modeling and simulation approach for oral dapagliflozin, a primarily uridine diphosphate-glucuronosyltransferase (UGT)-metabolized human sodium-glucose cotransporter 2 selective inhibitor. A mechanistic dapagliflozin physiologically based pharmacokinetic (PBPK) model was developed using in vitro metabolism and clinical pharmacokinetic (PK) data and verified for context of use (e.g., exposure predictions in pediatric subjects aged 1 month to 18 years). Dapagliflozin exposure is challenging to predict in pediatric populations owing to differences in UGT1A9 ontogeny maturation and paucity of clinical PK data in younger age groups. Based on the exposure-response relationship of dapagliflozin, twofold acceptance criteria were applied between model-predicted and observed drug exposures and PK parameters (area under the curve and maximum drug concentration) in various scenarios, including monotherapy in healthy adults (single/multiple dose), monotherapy in hepatically or renally impaired patients, and drug-drug interactions with UGT1A9 modulators, such as mefenamic acid and rifampin. The PBPK model captured the observed exposure within twofold of the observed monotherapy data in adults and adolescents and in special population. As a guide to determining dosing regimens in pediatric studies, the verified PBPK model, along with UGT enzyme ontogeny maturation understanding, was used for predictions of dapagliflozin monotherapy exposures in pediatric subjects aged 1 month to 18 years that best matched exposure in adult patients with a 10-mg single dose of dapagliflozin.