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.

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.

Multiplex Hybrid Antigen-Capture LC-MRM Quantification in Sera and Nasal Lining Fluid of AZD7442, a SARS-CoV-2-Targeting Antibody Combination.

AZD7442 (tixagevimab [AZD8895]/cilgavimab [AZD1061]) is a monoclonal antibody (mAb) combination in development for the prevention and treatment of coronavirus disease 2019. Traditionally, bioanalysis of mAbs is performed using ligand binding assays (LBAs), which offer sensitivity, robustness, and ease of implementation. However, LBAs frequently require generation of critical reagents that typically take several months.

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.

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.

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.

Efficacy and safety of intramuscular administration of tixagevimab-cilgavimab for early outpatient treatment of COVID-19 (TACKLE): a phase 3, randomised, double-blind, placebo-controlled trial.

Background: Early intramuscular administration of SARS-CoV-2-neutralising monoclonal antibody combination, tixagevimab-cilgavimab, to non-hospitalised adults with mild to moderate COVID-19 has potential to prevent disease progression. We aimed to evaluate the safety and efficacy of tixagevimab-cilgavimab in preventing progression to severe COVID-19 or death.

Methods: TACKLE is an ongoing, phase 3, randomised, double-blind, placebo-controlled study conducted at 95 sites in the USA, Latin America, Europe, and Japan.

Intramuscular AZD7442 (Tixagevimab-Cilgavimab) for Prevention of Covid-19.

Background: The monoclonal-antibody combination AZD7442 is composed of tixagevimab and cilgavimab, two neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that have an extended half-life and have been shown to have prophylactic and therapeutic effects in animal models. Pharmacokinetic data in humans indicate that AZD7442 has an extended half-life of approximately 90 days.

Methods: In an ongoing phase 3 trial, we enrolled adults (≥18 years of age) who had an increased risk of an inadequate response to vaccination against coronavirus disease 2019 (Covid-19), an increased risk of exposure to SARS-CoV-2, or both.

Population Pharmacokinetics of Cotadutide in Subjects with Type 2 Diabetes.

Background And Objectives: Cotadutide is a balanced dual glucagon-like peptide-1/glucagon receptor agonist under development for the treatment of nonalcoholic steatohepatitis and chronic kidney disease with type 2 diabetes. The objectives of the analysis were to characterize the population pharmacokinetics of cotadutide following daily subcutaneous injection in subjects with type 2 diabetes and to evaluate the effect of demographic and clinical variables of interest on cotadutide pharmacokinetics.

Methods: This study analyzed 8834 plasma concentrations of cotadutide from 759 subjects with type 2 diabetes who received daily subcutaneous doses from 20 to 600 μg from six clinical studies.

Association of circulating protein biomarkers with clinical outcomes of durvalumab in head and neck squamous cell carcinoma.

The potential for durvalumab, a programmed cell death ligand-1 (PD-L1)-blocking monoclonal antibody, to treat head and neck squamous cell carcinoma (HNSCC) is being evaluated in multiple clinical trials. We assessed circulating proteins at baseline to identify potential biomarkers and to understand pathways related to clinical outcomes for durvalumab. Prior to treatment, 66 serum proteins were measured using multiplex immunoassays for 158 durvalumab-treated HNSCC patients in the phase II HAWK and CONDOR trials as a discovery dataset and 209 durvalumab-treated HNSCC patients in the phase III EAGLE trial as a validation dataset.

Confirmatory cut point has limited ability to make accurate classifications in immunogenicity assays.

Competitive inhibition with excess unlabeled drug is used to confirm the presence of antidrug antibodies (ADA) in study samples. We evaluated specific and nonspecific responses from both drug-naive and drug-treated subjects to identify conditions required by the confirmatory assay to make accurate ADA classifications. Nonspecific signal measured in drug-naive samples used to determine assay cut points was uniformly low and close to the screening cut point.

From the Cover: Evaluation of the Effects of Tanezumab, a Monoclonal Antibody Against Nerve Growth Factor, on the Sympathetic Nervous System in Adult Cynomolgus Monkeys (Macaca fascicularis): A Stereologic, Histomorphologic, and Cardiofunctional Assessment.

Tanezumab, a humanized monoclonal antibody against nerve growth factor is in development for treatment of chronic pain. Three nonclinical studies assessed effects of clinically relevant and supratherapeutic doses of tanezumab on the sympathetic nervous system (SNS) of adult nonhuman primates. Study 1 evaluated potential effects of subcutaneous (SC) tanezumab (1.