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).

AZD7442 (Tixagevimab/Cilgavimab) for Post-Exposure Prophylaxis of Symptomatic Coronavirus Disease 2019.

BACKGROUND: This phase 3 trial assessed AZD7442 (tixagevimab/cilgavimab) for post-exposure prophylaxis against symptomatic coronavirus disease 2019 (COVID-19). METHODS: Adults without prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or COVID-19 vaccination were enrolled within 8 days of exposure to a SARS-CoV-2-infected individual and randomized 2:1 to a single 300-mg AZD7442 dose (one 1.5-mL intramuscular injection each of tixagevimab and cilgavimab) or placebo. Primary end points were safety and first post-dose SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR)-positive symptomatic COVID-19 event before day 183. RESULTS: A total of 1121 participants were randomized and dosed (AZD7442, n = 749; placebo, n = 372). Median (range) follow-up was 49 (5-115) and 48 (20-113) days for AZD7442 and placebo, respectively. Adverse events occurred in 162 of 749 (21.6%) and 111 of 372 (29.8%) participants with AZD7442 and placebo, respectively, mostly mild/moderate. RT-PCR-positive symptomatic COVID-19 occurred in 23 of 749 (3.1%) and 17 of 372 (4.6%) AZD7442- and placebo-treated participants, respectively (relative risk reduction, 33.3%; 95% confidence interval [CI], -25.9 to 64.7; P = .21). In predefined subgroup analyses of 1073 (96%) participants who were SARS-CoV-2 RT-PCR-negative (n = 974, 87%) or missing an RT-PCR result (n = 99, 9%) at baseline, AZD7442 reduced RT-PCR-positive symptomatic COVID-19 by 73.2% (95% CI, 27.1 to 90.1) vs placebo. CONCLUSIONS: This study did not meet the primary efficacy end point of post-exposure prevention of symptomatic COVID-19. However, analysis of participants who were SARS-CoV-2 RT-PCR-negative or missing an RT-PCR result at baseline support a role for AZD7442 in preventing symptomatic COVID-19. Clinical Trials Registration. NCT04625972.

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.

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. Instead, we developed a highly sensitive (5 ng/mL limit of quantification) method using a hybrid LBA-liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) approach for quantification of the two codosed antibodies in serum and nasal lining fluid (NLF), a rare matrix. The method was optimized by careful selection of multiple reaction monitoring, capture reagents, magnetic beads, chromatographic conditions, evaluations of selectivity, and matrix effect. The final assay used viral spike protein receptor-binding domain as capture reagent and signature proteotypic peptides from the complementarity-determining region of each mAb for detection. In contrast to other methods of similar/superior sensitivity, our approach did not require multidimensional separations and can be operated in an analytical flow regime, ensuring high throughput and robustness required for clinical analysis at scale. The sensitivity of this method significantly exceeds typical sensitivity of ∼100 ng/mL for analytical flow 1D LBA-LC-MS/MS methods for large macromolecules, such as antibodies. Furthermore, infection and vaccination status did not impact method performance, ensuring method robustness and applicability to a broad patient population. This report demonstrated the general applicability of the hybrid LBA-LC-MS/MS approach to platform quantification of antibodies with high sensitivity and reproducibility, with specialized extension to matrices of increasing interest, such as NLF.

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.

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.

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. Eligible participants were non-hospitalised adults aged 18 years or older with a laboratory-confirmed SARS-CoV-2 infection (determined by RT-PCR or an antigen test) from any respiratory tract specimen collected 3 days or less before enrolment and who had not received a COVID-19 vaccination. A WHO Clinical Progression Scale score from more than 1 to less than 4 was required for inclusion and participants had to receive the study drug 7 days or less from self-reported onset of mild to moderate COVID-19 symptoms or measured fever. Participants were randomly assigned (1:1) to receive either a single tixagevimab-cilgavimab 600 mg dose (two consecutive 3 mL intramuscular injections, one each of 300 mg tixagevimab and 300 mg cilgavimab) or placebo. Randomisation was stratified (using central blocked randomisation with randomly varying block sizes) by time from symptom onset, and high-risk versus low-risk of progression to severe COVID-19. Participants, investigators, and sponsor staff involved in the treatment or clinical evaluation and monitoring of the participants were masked to treatment-group assignments. The primary endpoints were severe COVID-19 or death from any cause through to day 29, and safety. This study is registered with ClinicalTrials.gov, NCT04723394. FINDINGS: Between Jan 28, 2021, and July 22, 2021, 1014 participants were enrolled, of whom 910 were randomly assigned to a treatment group (456 to receive tixagevimab-cilgavimab and 454 to receive placebo). The mean age of participants was 46·1 years (SD 15·2). Severe COVID-19 or death occurred in 18 (4%) of 407 participants in the tixagevimab-cilgavimab group versus 37 (9%) of 415 participants in the placebo group (relative risk reduction 50·5% [95% CI 14·6-71·3]; p=0·0096). The absolute risk reduction was 4·5% (95% CI 1·1-8·0; p<0·0001). Adverse events occurred in 132 (29%) of 452 participants in the tixagevimab-cilgavimab group and 163 (36%) of 451 participants in the placebo group, and were mostly of mild or moderate severity. There were three COVID-19-reported deaths in the tixagevimab-cilgavimab group and six in the placebo group. INTERPRETATION: A single intramuscular tixagevimab-cilgavimab dose provided statistically and clinically significant protection against progression to severe COVID-19 or death versus placebo in unvaccinated individuals and safety was favourable. Treating mild to moderate COVID-19 earlier in the disease course with tixagevimab-cilgavimab might lead to more favourable outcomes. FUNDING: AstraZeneca.

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. Participants were randomly assigned in a 2:1 ratio to receive a single dose (two consecutive intramuscular injections, one containing tixagevimab and the other containing cilgavimab) of either 300 mg of AZD7442 or saline placebo, and they were followed for up to 183 days in the primary analysis. The primary safety end point was the incidence of adverse events after a single dose of AZD7442. The primary efficacy end point was symptomatic Covid-19 (SARS-CoV-2 infection confirmed by means of reverse-transcriptase-polymerase-chain-reaction assay) occurring after administration of AZD7442 or placebo and on or before day 183. RESULTS: A total of 5197 participants underwent randomization and received one dose of AZD7442 or placebo (3460 in the AZD7442 group and 1737 in the placebo group). The primary analysis was conducted after 30% of the participants had become aware of their randomized assignment. In total, 1221 of 3461 participants (35.3%) in the AZD7442 group and 593 of 1736 participants (34.2%) in the placebo group reported having at least one adverse event, most of which were mild or moderate in severity. Symptomatic Covid-19 occurred in 8 of 3441 participants (0.2%) in the AZD7442 group and in 17 of 1731 participants (1.0%) in the placebo group (relative risk reduction, 76.7%; 95% confidence interval [CI], 46.0 to 90.0; P<0.001); extended follow-up at a median of 6 months showed a relative risk reduction of 82.8% (95% CI, 65.8 to 91.4). Five cases of severe or critical Covid-19 and two Covid-19-related deaths occurred, all in the placebo group. CONCLUSIONS: A single dose of AZD7442 had efficacy for the prevention of Covid-19, without evident safety concerns. (Funded by AstraZeneca and the U.S. government; PROVENT ClinicalTrials.gov number, NCT04625725.).

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. The impact of covariates on cotadutide pharmacokinetics was quantified, and body weight effect on cotadutide exposure was further evaluated using a simulation approach. The model performance was evaluated through prediction-corrected visual predictive checks. RESULTS: A one-compartment model with first-order absorption and elimination described cotadutide pharmacokinetic data well. The mean values for cotadutide apparent clearance, apparent distribution volume, absorption rate constant, and half-life were 1.04 L/h (interindividual variability [IIV]: 26.5%), 18.7 L (IIV: 28.7%), 0.343 h-1 (IIV: 38.6%), and 12.9 h, respectively. Higher body weight, lower albumin, and higher alanine aminotransferase were associated with an increase in cotadutide clearance, while an increase in anti-drug antibody titers was associated with a decrease in cotadutide clearance. These statistically significant effects were not considered clinically significant and did not warrant dose adjustment. Effects of other tested baseline covariates (age, sex, body mass index, hemoglobin A1c, renal function, duration of diabetes) were not found to statistically significantly affect cotadutide pharmacokinetics. CONCLUSIONS: Cotadutide pharmacokinetics was adequately described by a one-compartment linear model with first-order absorption and elimination. Body weight-based dosing is not necessary for cotadutide based on the simulation using the final population pharmacokinetic modeling. This model will be used to evaluate exposure-response relationships for efficacy and safety in different indications that are being studied for cotadutide.

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.