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. Model simulations further enabled the identification of critical parameters that influence API exposure in tumors and downstream efficacy outcomes upon nanoparticle administration. The model was utilized to explore a range of dosing schedules and their effect on tumor growth kinetics, demonstrating the improved antitumor activity of nanoparticles with less frequent dosing compared to the same dose of naked APIs in conventional formulations.

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 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.

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.

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. A patient-derived xenograft (PDX) mouse model with EGFRm, MET-amplified NSCLC was tested with a fixed osimertinib dose [10 mg/kg for exposures equivalent to (≈)80 mg], combined with doses of savolitinib (0-15 mg/kg, ≈0-600 mg once daily), both with 1-aminobenzotriazole (to better match clinical half-life). After 20 days of oral dosing, samples were taken at various time points to follow the time course of drug exposure in addition to phosphorylated MET and EGFR (pMET and pEGFR) change. Population pharmacokinetics, savolitinib concentration versus percentage inhibition from baseline in pMET, and the relationship between pMET and tumor growth inhibition (TGI) were also modeled. As single agents, savolitinib (15 mg/kg) showed significant antitumor activity, reaching ∼84% TGI, and osimertinib (10 mg/kg) showed no significant antitumor activity (34% TGI, P > 0.05 vs. vehicle). Upon combination, at a fixed dose of osimertinib, significant savolitinib dose-related antitumor activity was shown, ranging from 81% TGI (0.3 mg/kg) to 84% tumor regression (15 mg/kg). Pharmacokinetic-pharmacodynamic modeling showed that the maximum inhibition of both pEGFR and pMET increased with increasing savolitinib doses. Savolitinib demonstrated exposure-related combination antitumor activity when combined with osimertinib in the EGFRm MET-amplified NSCLC PDX model.

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. The PBPK model, parameterized either by considering species-specific physiology or using alternate scaling methods such as allometry, was shown to capture exposure profiles across species. A sensitivity analysis highlighted API systemic clearance as a key parameter influencing released API levels. The PBPK model was utilized to simulate human exposure profiles, which overlaid dose-normalized data from mouse, rat and dog. The consistency in measured interspecies exposures as well as the capability of the PBPK model to simulate observed dynamics support its use as a powerful translational tool.

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. Intravenous infusion using vascular access button (VAB) technology was found to best reproduce clinical target exposures of AZD5991 in mice. Exposure-efficacy relationships were evaluated, demonstrating that dissimilar PK profiles result in differences in target engagement and efficacy outcomes. Thus, these data underscore the importance of accurately ascribing key PK metrics in the translational process to enable clinically meaningful predictions of efficacy.

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. 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.

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. 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 (Cmax) 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.

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. EXPERIMENTAL DESIGN: A PKPD model was developed for AZD3229 linking plasma concentrations to inhibition of phosphorylated KIT using data generated from several in vivo preclinical tumor models, and in vitro data generated in a panel of Ba/F3 cell lines. RESULTS: AZD3229 drives inhibition of phosphorylated KIT in an exposure-dependent manner, and optimal efficacy is observed when >90% inhibition of KIT phosphorylation is sustained over the dosing interval. Integrating the predicted human pharmacokinetics into the mouse PKPD model predicts that an oral twice daily human dose greater than 34 mg is required to ensure adequate coverage across the mutations investigated. Benchmarking shows that compared with standard-of-care KIT inhibitors, AZD3229 has the potential to deliver the required target coverage across a wider spectrum of primary or secondary mutations. CONCLUSIONS: We demonstrate that AZD3229 warrants clinical investigation as a new treatment for patients with GIST based on its ability to inhibit both ATP-binding and A-loop mutations of KIT at clinically relevant exposures.

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. Starting from a series of previously reported phenoxyquinazoline and quinoline based inhibitors of the tyrosine kinase PDGFRα, potency against a diverse panel of mutant KIT driven Ba/F3 cell lines was optimized, with a particular focus on reducing activity against a KDR driven cell model in order to limit the potential for hypertension commonly seen in second and third line GIST therapies. AZD3229 demonstrates potent single digit nM growth inhibition across a broad cell panel, with good margin to KDR-driven effects. Selectivity over KDR can be rationalized predominantly by the interaction of water molecules with the protein and ligand in the active site, and its kinome selectivity is similar to the best of the approved GIST agents. This compound demonstrates excellent cross-species pharmacokinetics, shows strong pharmacodynamic inhibition of target, and is active in several in vivo models of GIST.

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. 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.

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. We determined the pharmacodynamic and antitumor response of the selective MET inhibitor AZD6094 in two PRCC patient-derived xenograft (PDX) models. EXPERIMENTAL DESIGN: Two PRCC PDX models were identified and MET mutation status and copy number determined. Pharmacodynamic and antitumor activity of AZD6094 was tested using a dose response up to 25 mg/kg daily, representing clinically achievable exposures, and compared with the activity of the RCC standard-of-care sunitinib (in RCC43b) or the multikinase inhibitor crizotinib (in RCC47). RESULTS: AZD6094 treatment resulted in tumor regressions, whereas sunitinib or crizotinib resulted in unsustained growth inhibition. Pharmacodynamic analysis of tumors revealed that AZD6094 could robustly suppress pMET and the duration of target inhibition was dose related. AZD6094 inhibited multiple signaling nodes, including MAPK, PI3K, and EGFR. Finally, at doses that induced tumor regression, AZD6094 resulted in a dose- and time-dependent induction of cleaved PARP, a marker of cell death. CONCLUSIONS: Data presented provide the first report testing therapeutics in preclinical in vivo models of PRCC and support the clinical development of AZD6094 in this indication.

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.

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. AZD3514 inhibited testosterone-driven seminal vesicle development in juvenile male rats and the growth of androgen-dependent Dunning R3327H prostate tumors in adult rats. Furthermore, this class of compound showed antitumor activity in the HID28 mouse model of CRPC in vivo. AZD3514 is currently in phase I clinical evaluation.

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.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution (Vss ) were predicted by unbound fraction corrected intercept method (FCIM) and Øie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog ) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css -MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria.

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. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (Cmax and AUC0- t ). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first-pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro-input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods.