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Background and Purpose: Savolitinib (AZD6094, HMPL‐504, volitinib) is an oral, potent, and highly MET receptor TK inhibitor. This series of studies aimed to develop a pharmacokinetic–pharmacodynamic (PK/PD) model to link inhibition of MET phosphorylation (pMET) by savolitinib with anti‐tumour activity. Experimental Approach Cell line‐derived xenograft (CDX) experiments using human lung cancer (EBC‐1) and gastric cancer (MKN‐45) cells were conducted in athymic nude mice using a variety of doses and schedules of savolitinib. Tumour pMET changes and growth inhibition were calculated after 28 days. Population PK/PD techniques were used to construct a PK/PD model for savolitinib. Key Results: Savolitinib showed dose‐ and dose frequency‐dependent anti‐tumour activity in the CDX models, with more frequent, lower dosing schedules (e.g., twice daily) being more effective than intermittent, higher dosing schedules (e.g., 4 days on/3 days off or 2 days on/5 days off). There was a clear exposure–response relationship, with maximal suppression of pMET of >90%. Data from additional CDX and patient‐derived xenograft (PDX) models overlapped, allowing calculation of a single EC50 of 0.38 ng·ml−1. Tumour growth modelling demonstrated that prolonged, high levels of pMET inhibition (>90%) were required for tumour stasis and regression in the models. Conclusion and Implications: High and persistent levels of MET inhibition by savolitinib were needed for optimal monotherapy anti‐tumour activity in preclinical models. The modelling framework developed here can be used to translate tumour growth inhibition from the mouse to human and thus guide choice of clinical dose and schedule. [ABSTRACT FROM AUTHOR]

During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (C max) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs. It is now universally recognized that an acceptable human PK profile increases the probability of a candidate drug becoming a successful therapy. A variety of tools are available to predict human PK behavior in advance of clinical data, including scaling clearance from in vitro metabolic stability data, and physiologically based scaling of volume of distribution. To improve PK prediction methods, it is crucial to continually assess the performance of predictions with first-time-in-human PK data for new candidate drugs. To date, AstraZeneca have compared observed PK to predictions for 116 candidate drugs, and since the launch of our five-dimensional framework in 2011, we have driven sustained improvements in the quality of PK predictions. [ABSTRACT FROM AUTHOR]

In vivo models have been crucial for developing our understanding of key processes associated with human disease and developing novel therapeutics. These in vivo studies are becoming increasingly complex, requiring long-term efficacy data and additional supportive datasets such as pharmacokinetic profiles and analysis of multiple biomarkers of pharmacodynamic response and efficacy. Moreover, a new agent will be investigated in many different models and often in combination with other drugs. Despite advances across the industry integrating and analysing complex datasets, management of in vivo data remains an ongoing challenge across the industry. Here, we describe a project that has successfully delivered a working solution to integrate pharmacokinetic, biomarker and efficacy data, independent of therapy area. [ABSTRACT FROM AUTHOR]

Trastuzumab deruxtecan (T‐DXd; DS‐8201; ENHERTU®) is a human epithelial growth factor receptor 2 (HER2)‐directed antibody drug conjugate (ADC) with demonstrated antitumor activity against a range of tumor types. Aiming to understand the relationship between antigen expression and downstream efficacy outcomes, T‐DXd was administered in tumor‐bearing mice carrying NCI‐N87, Capan‐1, JIMT‐1, and MDA‐MB‐468 xenografts, characterized by varying HER2 levels. Plasma pharmacokinetics (PK) of total antibody, T‐DXd, and released DXd and tumor concentrations of released DXd were evaluated, in addition to monitoring γΗ2AX and pRAD50 pharmacodynamic (PD) response. A positive relationship was observed between released DXd concentrations in tumor and HER2 expression, with NCI‐N87 xenografts characterized by the highest exposures compared to the remaining cell lines. γΗ2AX and pRAD50 demonstrated a sustained increase over several days occurring with a time delay relative to tumoral‐released DXd concentrations. In vitro investigations of cell‐based DXd disposition facilitated the characterization of DXd kinetics across tumor cells. These outputs were incorporated into a mechanistic mathematical model, utilized to describe PK/PD trends. The model captured plasma PK across dosing arms as well as tumor PK in NCI‐N87, Capan‐1, and MDA‐MB‐468 models; tumor concentrations in JIMT‐1 xenografts required additional parameter adjustments reflective of complex receptor dynamics. γΗ2AX longitudinal trends were well characterized via a unified PD model implemented across xenografts demonstrating the robustness of measured PD trends. This work supports the application of a mechanistic model as a quantitative tool, reliably projecting tumor payload concentrations upon T‐DXd administration, as the first step towards preclinical‐to‐clinical translation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Implementation of quantitative pharmacology strategy to discovery drug projects. [•] Building dedicated preclinical M&S capability. [•] Developing a companywide infrastructure for flexible data management and use. [•] Facilitation of rational decision making, increased efficiency and reduced cycle times and costs. [Copyright &y& Elsevier]

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities. [ABSTRACT FROM AUTHOR]

Abstract: Removal of the basic piperazine nitrogen atom, introduction of a solubilising end group and partial reduction of the triazolopyridazine moiety in the previously-described lead androgen receptor downregulator 6-[4-(4-cyanobenzyl)piperazin-1-yl]-3-(trifluoromethyl)[1,2,4]triazolo[4,3-b]pyridazine (1) addressed hERG and physical property issues, and led to clinical candidate 6-(4-{4-[2-(4-acetylpiperazin-1-yl)ethoxy]phenyl}piperidin-1-yl)-3-(trifluoromethyl)-7,8-dihydro[1,2,4]triazolo[4,3-b]pyridazine (12), designated AZD3514, that is being evaluated in a Phase I clinical trial in patients with castrate-resistant prostate cancer. [Copyright &y& Elsevier]