Quantitative evaluation of trastuzumab deruxtecan pharmacokinetics and pharmacodynamics in mouse models of varying degrees of HER2 expression.

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 PKPD Case Study: Achieving Clinically Relevant Exposures of AZD5991 in Oncology Mouse Models.

Capturing human equivalent drug exposures preclinically is a key challenge in the translational process. Motivated by the need to recapitulate the pharmacokinetic (PK) profile of the clinical stage Mcl-1 inhibitor AZD5991 in mice, we describe the methodology used to develop a refined mathematical model relating clinically relevant concentration profiles to efficacy. Administration routes were explored to achieve target exposures matching the clinical exposure of AZD5991.

Interspecies evaluation of a physiologically based pharmacokinetic model to predict the biodistribution dynamics of dendritic nanoparticles.

The exposure of a dendritic nanoparticle and its conjugated active pharmaceutical ingredient (API) was determined in mouse, rat and dog, with the aim of investigating interspecies differences facilitating clinical translation. Plasma area under the curves (AUCs) were found to be dose proportional across species, while dose normalized concentration time course profiles in plasma, liver and spleen were superimposable in mouse, rat and dog. A physiologically based pharmacokinetic (PBPK) model, previously developed for mouse, was evaluated as a suitable framework to prospectively capture concentration dynamics in rat and dog.

Pharmacokinetic/Pharmacodynamic Analysis of Savolitinib plus Osimertinib in an EGFR Mutation-Positive, MET-Amplified Non-Small Cell Lung Cancer Model.

Osimertinib is a third-generation, irreversible, oral EGFR tyrosine kinase inhibitor (TKI) recommended as first-line treatment for patients with locally advanced/metastatic EGFR mutation-positive (EGFRm) non-small cell lung cancer (NSCLC). However, MET amplification/overexpression is a common acquired osimertinib resistance mechanism. Savolitinib is an oral, potent, and highly selective MET-TKI; preliminary data suggest that combining osimertinib with savolitinib may overcome MET-driven resistance.

Quantitative Evaluation of Dendritic Nanoparticles in Mice: Biodistribution Dynamics and Downstream Tumor Efficacy Outcomes.

A physiologically based pharmacokinetic model was developed to describe the tissue distribution kinetics of a dendritic nanoparticle and its conjugated active pharmaceutical ingredient (API) in plasma, liver, spleen, and tumors. Tumor growth data from MV-4-11 tumor-bearing mice were incorporated to investigate the exposure/efficacy relationship. The nanoparticle demonstrated improved antitumor activity compared to the conventional API formulation, owing to the extended released API concentrations at the site of action.

A pharmacokinetic-pharmacodynamic model for the MET tyrosine kinase inhibitor, savolitinib, to explore target inhibition requirements for anti-tumour activity.

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.

Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades.

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.

The Pharmacokinetic-Pharmacodynamic (PKPD) Relationships of AZD3229, a Novel and Selective Inhibitor of KIT, in a Range of Mouse Xenograft Models of GIST.

Purpose: The emergence of secondary mutations is a cause of resistance to current KIT inhibitors used in the treatment of patients with gastrointestinal stromal tumors (GIST). AZD3229 is a selective inhibitor of wild-type KIT and a wide spectrum of primary and secondary mutations seen in patients with GIST. The objective of this analysis is to establish the pharmacokinetic-pharmacodynamic (PKPD) relationship of AZD3229 in a range of mouse GIST tumor models harboring primary and secondary KIT mutations, and to benchmark AZD3229 against other KIT inhibitors.

Further Considerations Towards an Effective and Efficient Oncology Drug Discovery DMPK Strategy.

Background: DMPK data and knowledge are critical in maximising the probability of developing successful drugs via the application of in silico, in vitro and in vivo approaches in drug discovery.

Methods: The evaluation, optimisation and prediction of human pharmacokinetics is now a mainstay within drug discovery. These elements are at the heart of the 'right tissue' component of AstraZeneca's '5Rs framework' which, since its adoption, has resulted in increased success of Phase III clinical trials.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities.

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.

Discovery of N-(4-{[5-Fluoro-7-(2-methoxyethoxy)quinazolin-4-yl]amino}phenyl)-2-[4-(propan-2-yl)-1 H-1,2,3-triazol-1-yl]acetamide (AZD3229), a Potent Pan-KIT Mutant Inhibitor for the Treatment of Gastrointestinal Stromal Tumors.

While the treatment of gastrointestinal stromal tumors (GISTs) has been revolutionized by the application of targeted tyrosine kinase inhibitors capable of inhibiting KIT-driven proliferation, diverse mutations to this kinase drive resistance to established therapies. Here we describe the identification of potent pan-KIT mutant kinase inhibitors that can be dosed without being limited by the tolerability issues seen with multitargeted agents. This effort focused on identification and optimization of an existing kinase scaffold through the use of structure-based design.

Developing PreDICT - a fully integrated data platform for preclinical in vivo data: learning from experience.

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.

The MET Inhibitor AZD6094 (Savolitinib, HMPL-504) Induces Regression in Papillary Renal Cell Carcinoma Patient-Derived Xenograft Models.

Purpose: Papillary renal cell carcinoma (PRCC) is the second most common cancer of the kidney and carries a poor prognosis for patients with nonlocalized disease. The HGF receptor MET plays a central role in PRCC and aberrations, either through mutation, copy number gain, or trisomy of chromosome 7 occurring in the majority of cases. The development of effective therapies in PRCC has been hampered in part by a lack of available preclinical models.

AZD3514: a small molecule that modulates androgen receptor signaling and function in vitro and in vivo.

Continued androgen receptor (AR) expression and signaling is a key driver in castration-resistant prostate cancer (CRPC) after classical androgen ablation therapies have failed, and therefore remains a target for the treatment of progressive disease. Here, we describe the biological characterization of AZD3514, an orally bioavailable drug that inhibits androgen-dependent and -independent AR signaling. AZD3514 modulates AR signaling through two distinct mechanisms, an inhibition of ligand-driven nuclear translocation of AR and a downregulation of receptor levels, both of which were observed in vitro and in vivo.

Model-based drug discovery: implementation and impact.

Model-based drug discovery (MBDDx) aims to build and continuously improve the quantitative understanding of the relation between drug exposure (target engagement) efficacy and safety, to support target validation; to define compound property criteria for lead optimization and safety margins; to set the starting dose; and to predict human dose and scheduling for clinical candidates alone, or in combination with other medicines. AstraZeneca has systematically implemented MBDDx within all drug discovery programs, with a focused investment to build a preclinical modeling and simulation capability and an in vivo information platform and architecture, the implementation, impact and learning of which are discussed here.

Discovery of AZD3514, a small-molecule androgen receptor downregulator for treatment of advanced prostate cancer.

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.

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach.

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration-time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach.

The objective of this study was to evaluate the performance of the Wajima allometry (Css -MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.