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).

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.

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.

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.

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.

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.

A Laboratory-Specific Scaling Factor to Predict the In Vivo Human Clearance of Aldehyde Oxidase Substrates.

Aldehyde oxidase (AO) efficiently metabolizes a range of compounds with N-containing heterocyclic aromatic rings and/or aldehydes. The limited knowledge of AO activity and abundance (in vitro and in vivo) has led to poor prediction of in vivo systemic clearance (CL) using in vitro-to-in vivo extrapolation approaches, which for drugs in development can lead to their discontinuation. We aimed to identify appropriate scaling factors to predict AO CL of future new chemical entities (NCEs). The metabolism of six AO substrates was measured in human liver cytosol (HLC) and S9 fractions. Measured blood-to-plasma ratios and free fractions (in the in vitro system and in plasma) were used to develop physiologically based pharmacokinetic models for each compound. The impact of extrahepatic metabolism was explored, and the intrinsic clearance required to recover in vivo profiles was estimated and compared with in vitro measurements. Using HLC data and assuming only hepatic metabolism, a systematic underprediction of clearance was observed (average fold underprediction was 3.8). Adding extrahepatic metabolism improved the accuracy of the results (average fold error of 1.9). A workflow for predicting metabolism of an NCE by AO is proposed, and an empirical (laboratory-specific) scaling factor of three on the predicted intravenous CL allows a reasonable prediction of the available clinical data. Alternatively, considering also extrahepatic metabolism, an scaling factor of 6.5 applied on the intrinsic clearance could be used. Future research should focus on the impact of the in vitro study designs and the contribution of extrahepatic metabolism to AO-mediated clearance to understand the mechanisms behind the systematic underprediction. SIGNIFICANCE STATEMENT: This works describes the development of scaling factors to allow in vitro-in vivo extrapolation of the clearance of compounds by aldehyde oxidase metabolism in humans. In addition, physiologically based pharmacokinetic models were developed for each of the aldehyde oxidase substrate compounds investigated.

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

OBJECTIVES: To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. METHODS: A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. RESULTS: Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. CONCLUSIONS: The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Dopamine D2 receptor occupancy as a predictor of catalepsy in rats: a pharmacokinetic-pharmacodynamic modeling approach.

OBJECTIVES: Dopamine D2 receptor occupancy (D2RO) is the major determinant of efficacy and safety in schizophrenia drug therapy. Excessive D2RO (>80%) is known to cause catalepsy (CAT) in rats and extrapyramidal side effects (EPS) in human. The objective of this study was to use pharmacokinetic and pharmacodynamic modeling tools to relate CAT with D2RO in rats and to compare that with the relationship between D2RO and EPS in humans. METHODS: Severity of CAT was assessed in rats at hourly intervals over a period of 8 h after antipsychotic drug treatment. An indirect response model with and without Markov elements was used to explain the relationship of D2RO and CAT. RESULTS: Both models explained the CAT data well for olanzapine, paliperidone and risperidone. However, only the model with the Markov elements predicted the CAT severity well for clozapine and haloperidol. The relationship between CAT scores in rat and EPS scores in humans was implemented in a quantitative manner. Risk of EPS not exceeding 10% over placebo correlates with less than 86% D2RO and less than 30% probability of CAT events in rats. CONCLUSION: A quantitative relationship between rat CAT and human EPS was elucidated and may be used in drug discovery to predict the risk of EPS in humans from D2RO and CAT scores measured in rats.

Population pharmacokinetic-pharmacodynamic modeling of haloperidol in patients with schizophrenia using positive and negative syndrome rating scale.

The aim of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model that quantifies the efficacy of haloperidol, accounting for the placebo effect, the variability in exposure-response, and the dropouts. Subsequently, the developed model was utilized to characterize an effective dosing strategy for using haloperidol as a comparator drug in future antipsychotic drug trials. The time course of plasma haloperidol concentrations from 122 subjects and the Positive and Negative Syndrome Scale (PANSS) scores from 473 subjects were used in this analysis. A nonlinear mixed-effects modeling approach was utilized to describe the time course of PK and PANSS scores. Bootstrapping and simulation-based methods were used for the model evaluation. A 2-compartment model adequately described the haloperidol PK profiles. The Weibull and Emax models were able to describe the time course of the placebo and the drug effects, respectively. An exponential model was used to account for dropouts. Joint modeling of the PKPD model with dropout model indicated that the probability of patients dropping out is associated with the observed high PANSS score. The model evaluation results confirmed that the precision and accuracy of parameter estimates are acceptable. Based on the PKPD analysis, the recommended oral dose of haloperidol to achieve a 30% reduction in PANSS score from baseline is 5.6 mg/d, and the corresponding steady-state effective plasma haloperidol exposure is 2.7 ng/mL. In conclusion, the developed model describes the time course of PANSS scores adequately, and a recommendation of haloperidol dose was derived for future antipsychotic drug trials.

Modelling and simulation of placebo effect: application to drug development in schizophrenia.

High and variable placebo effect (PE) within and among clinical trials can substantially affect conclusions about the efficacy of new drugs in the treatment of schizophrenia and other neuropsychiatric disorders. In recent years, it has become increasingly difficult to prove drug efficacy against placebo, and one of the reasons is that the placebo response has increased over recent years. The increased placebo response over the years is partly explained by unidentified parallel interventions, patient factors, issues with trial designs, and regional variability or demographic differences. In addition, a nocebo effect, which is undesirable effects a subject manifests after receiving placebo, e.g. extrapyramidal side effects, in placebo arms of antipsychotic trials could also influence the PE and clinical trial outcomes. Placebo effects (PEs) are a natural phenomenon and cannot be avoided completely in clinical trials. However, accounting for the PE via mixed effects modelling approaches could reduce bias in quantifying the overall effect size of the drug treatment. This review article focuses on the PE and its impact on schizophrenia clinical trial outcomes. The authors briefly describe the factors that lead to high and variable PE. Next, pharmacometric approaches to account for the PE and dropouts in schizophrenia clinical trials are described. Finally, some points are provided that could be considered while designing and optimizing antipsychotic trials via simulation approaches.

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.

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.

Pharmacokinetic-pharmacodynamic modeling of the D2 and 5-HT (2A) receptor occupancy of risperidone and paliperidone in rats.

PURPOSE: A pharmacokinetic-pharmacodynamic (PK-PD) model was developed to describe the time course of brain concentration and dopamine D2 and serotonin 5-HT(2A) receptor occupancy (RO) of the atypical antipsychotic drugs risperidone and paliperidone in rats. METHODS: A population approach was utilized to describe the PK-PD of risperidone and paliperidone using plasma and brain concentrations and D2 and 5-HT(2A) RO data. A previously published physiology- and mechanism-based (PBPKPD) model describing brain concentrations and D2 receptor binding in the striatum was expanded to include metabolite kinetics, active efflux from brain, and binding to 5-HT(2A) receptors in the frontal cortex. RESULTS: A two-compartment model best fit to the plasma PK profile of risperidone and paliperidone. The expanded PBPKPD model described brain concentrations and D2 and 5-HT(2A) RO well. Inclusion of binding to 5-HT(2A) receptors was necessary to describe observed brain-to-plasma ratios accurately. Simulations showed that receptor affinity strongly influences brain-to-plasma ratio pattern. CONCLUSION: Binding to both D2 and 5-HT(2A) receptors influences brain distribution of risperidone and paliperidone. This may stem from their high affinity for D2 and 5-HT(2A) receptors. Receptor affinities and brain-to-plasma ratios may need to be considered before choosing the best PK-PD model for centrally active drugs.

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.

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.

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.