Rhys D.O. Jones

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.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: Comparative assessement of prediction methods of human clearance

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: Comparative assessment of prediction methods of human volume of distribution

The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state (Vss ). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis. In vivo and in vitro preclinical data were used to predict Vss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss . Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, for many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. It is recommended to initially use the in vitro tissue composition-based equations to predict Vss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human Vss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Øie-Tozer, the rat -dog-human proportionality equation, and the lumped-PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human Vss from several methods available in the literature.

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. Mol Cancer Ther; 12(9); 1715–27. ©2013 AACR.

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.

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 1: Goals, Properties of the Phrma Dataset, and Comparison with Literature Datasets

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.

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.

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. Clin Cancer Res; 21(12); 2811–9. ©2015 AACR.

Discovery of N-{4-[(6,7-dimethoxyquinazolin-4-yl)oxy]phenyl}-2-[4-(propan-2-yl)-1H-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.