Venkatesh Pilla Reddy

Antibody‐drug conjugates (ADCs) are among the most pharmacologically complex therapeutics in clinical development. Their behavior emerges from the interplay of antibody disposition, linker stability, payload pharmacology, and host physiology—interdependencies that render qualitative descriptors such as “stronger affinity” or “more potent payload” insufficient for reliable prediction of preclinical and clinical outcomes. Despite growing investment in quantitative medicine across drug development, no unified framework exists to guide quantitative decision‐making across the ADC design–development–dose‐optimization continuum. In this review we survey how fit‐for‐purpose quantitative approaches inform decisions at each stage of the development path, built on multi‐analyte data pipelines that capture the coupled pharmacokinetics (PK) of antibody, linker‐payload, and released payload. We highlight the utility of PBPK‐driven (physiologically‐based pharmacokinetics) feasibility and mechanistic sensitivity analyses for candidate triage and allometric and mechanism‐based translation for human exposure predictions. We also present a stepwise construction of exposure–response relationships bridging preclinical and clinical data and integration of efficacy and safety into a clinical utility index for dose optimization. Throughout, we argue that the central challenge for quantitative ADC development is not the absence of sophisticated models, but the disciplined integration of fit‐for‐purpose approaches into the decisions that shape the development path. We synthesize these ideas in the proposition of a preclinical clinical utility index (pCUI) as an efficacious lens for model‐informed ADC development: an integrative benefit–risk framework expanding on the established clinical utility index (CUI) that quantitatively assesses whether a therapeutic window plausibly exists before first‐in‐human dosing.

The ICH M12 Guideline on Drug Interaction Studies is the result of a harmonization process led by global regulatory and industry experts with experience in drug–drug interaction (DDI) assessments and interpretation. The Expert Working Group (EWG) built on areas of regional consensus and identified solutions to topics lacking initial consensus. This article describes the topics addressed in the guideline, with emphasis on areas that required extensive discussion. It mentions topics that were the subject of comments during the public consultation period. The scope of the guideline is pharmacokinetic DDIs mediated by metabolic enzymes and drug transporters. It describes in vitro and clinical DDI studies and predictive modeling evaluations conducted during drug development. The understanding of DDI liability, in the context of the intended patient population, guides the development of risk management strategies. In the in vitro area, this article describes the considerations that support the use of experimentally measured fraction unbound for drugs with > 99% protein binding, modification of several in vitro criteria used to recommend a clinical DDI study and modification of DDI assessment for metabolites. Areas of close attention by the EWG for clinical evaluation included the use of endogenous biomarker studies, the use of nested DDI studies, and the establishment of no‐effect boundaries. The article indicates the value of describing a general process for evaluating UGT‐mediated DDIs, although specific criteria are not available. The guideline describes the current understanding of the role of predictive modeling in DDI evaluation. The topics described in this article can stimulate further growth in the science of DDI assessments.

Antibody–drug conjugates (ADCs) are a promising class of targeted cancer therapies. However, they need careful dose optimization to maximize effectiveness and minimize side effects. Sometimes, safety issues may only become apparent after approval, so ongoing evaluation is important. This study aimed to assess the benefit–risk profiles of two approved trastuzumab–drug conjugates: trastuzumab emtansine (T‐DM1) and trastuzumab deruxtecan (T‐DXd). A systematic search of MEDLINE on May 1, 2024, identified clinical trials reporting the pharmacokinetics, pharmacodynamics, safety, and efficacy of T‐DM1 and T‐DXd. Summary‐level data from 103 trials was used along with model‐based meta‐analysis to develop population pharmacokinetic and exposure–response models for both ADCs. The study combined the objective response rate (ORR) and dose‐limiting toxicity (DLT) into a composite score called the clinical utility index (CUI) to determine optimal drug exposures and doses that maximize the benefit–risk balance. Different ORR/DLT weights and CUI thresholds representing desired minimum effect size were tested in three scenarios (“only phase I trials,” “phase I/II trials,” and “all phases”). Using a CUI threshold of 10%, the approved T‐DM1 dose of 3.6 mg/kg for breast cancer was found to align with an efficacy–safety (ORR‐DLT) ratio of 81:19 with all phases. Applying these weights to the T‐DXd analyses successfully predicted the approved T‐DXd dose (5.4 mg/kg, breast cancer), showing a CUI improvement compared to the 3.2 mg/kg dose of 8.3%, 8.2%, and 2.4% in the three respective scenarios. Overall, this proof‐of‐concept assessment of ADCs can save time and costs for pharmaceutical companies and optimize dosing to maximize patient benefit.

Antibody-drug conjugates (ADCs) are novel therapeutics combining two molecules linked together: an antibody that provides targeting to specific cells, and a cytotoxic drug (warhead) that can deconjugate from the antibody and kill those cells. The warhead by itself would be too nonspecific and toxic; the antibody by itself would be insufficiently effective at killing the cells. As more and more ADCs enter the drug development pipeline, understanding the mechanistic reasons behind efficacy and toxicity is critical to evaluating both successful and failed trials. Here, we have developed a mechanistic computational model of ADCs, and specifically parameterize it using data for MEDI2228, an anti-BCMA antibody conjugated to pyrrolobenzodiazepine (PBD) warheads. We build the model to track not only the concentrations of ADC and its released warhead drug inside and outside the cell, but also to track the recent history of the warhead, so that we can distinguish between pathways for on-target and bystander (nontargeted) cell killing. We show that this effect is predicted to be small under in vitro conditions due to dilution in large volumes of media, but likely to form a significant part of both targeted and nontargeted cell killing in vivo , where the extracellular volume has less of a dilution effect. We also explore the impact of key design parameters of ADCs, including drug to antibody ratio (DAR), warhead potency, and lipophilicity; this analysis demonstrates the balance needed between killing of targeted and nontargeted cells. Using this quantitative systems pharmacology model, we can generate insights for optimization of ADC design and determine which factors are most critical to efficacy and toxicity, leading to more informed and rational development of cancer therapies.

Physiologically‐based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug–drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well‐characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, f_m,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of f_m,CYP3A4 (0.086–1.0), Fg (0.11–1.0) and hepatic availability (0.09–0.96) were included. Predictions were most often accurate for compounds that are not P‐gp substrates or that are P‐gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first‐order absorption model might be more appropriate for a P‐gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P‐gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

Pregnancy is associated with various physiological changes that can significantly impact the disposition of drugs. To further the insight into how pregnancy affects the pharmacokinetics of drugs at different stages, clinical studies can be simulated using Physiologically Based Pharmacokinetic modelling. PBPK modelling of drugs metabolised by Phase I enzymes (CYPs) in pregnant population models had been reported in the past, while its use in Phase II (UGTs) is not known. In this study, based on the results of a recent meta-analysis, lamotrigine (UGT1A4) and raltegravir (UGT1A1) were selected as candidate drugs, and pregnancy-specific models were developed for both using the Simcyp v.21 simulator. A middle-out strategy was used where previously published drug parameters were adapted from a minimal to a full PBPK model to allow their application for the pregnancy population models using Simcyp PBPK software. Adapted models were successfully validated against observed clinical data both qualitatively (visual overlay of plasma concentrations on graphs) and quantitatively (calculating the predicted/observed ratios for AUC, Cmax and CL as well as statistical analysis using model prediction power metrics). They were then applied to predict the PKs of both drugs in pregnancy population models. The temporal changes in maternal enzymatic activities during gestation were modelled based on in vitro data reported in literature and default relationships encoded in the Simcyp platform for UGT1A1 and UGT1A4, respectively. Our study demonstrates the successful development and validation of a PBPK model for LTG and RTG in pregnancy population models. Future work with additional UGT1A4 substrate drugs using the proposed changes in UGT1A4 activity may enable validating the pregnancy population model and its subsequent use for the prospective prediction of PK.

Introduction We assessed effects of AZD7442 (tixagevimab/cilgavimab) on deaths from any cause or hospitalizations due to coronavirus disease 2019 (COVID-19) and symptom severity and longer-term safety in the TACKLE adult outpatient treatment study. Methods Participants received 600 mg AZD7442 (n = 452) or placebo (n = 451) ≤ 7 days of COVID-19 symptom onset. Results Death from any cause or hospitalization for COVID-19 complications or sequelae through day 169 (key secondary endpoint) occurred in 20/399 (5.0%) participants receiving AZD7442 versus 40/407 (9.8%) receiving placebo [relative risk reduction (RRR) 49.1%; 95% confidence interval (CI) 14.5, 69.7; p = 0.009] or 50.7% (95% CI 17.5, 70.5; p = 0.006) after excluding participants unblinded before day 169 for consideration of vaccination). AZD7442 reduced progression of COVID-19 symptoms versus placebo through to day 29 (RRR 12.5%; 95% CI 0.5, 23.0) and improved most symptoms within 1–2 weeks. Over median safety follow-up of 170 days, adverse events occurred in 174 (38.5%) and 196 (43.5%) participants receiving AZD7442 or placebo, respectively. Cardiac serious adverse events occurred in two (0.4%) and three (0.7%) participants receiving AZD7442 or placebo, respectively. Conclusions AZD7442 was well tolerated and reduced hospitalization and mortality through 6 months, and symptom burden through 29 days, in outpatients with mild-to-moderate COVID-19. Clinical Trial Registration Clinicaltrials.gov, NCT04723394. (https://beta.clinicaltrials.gov/study/NCT04723394). Graphical Abstract