Donald Heald

From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

A case example is presented in which the physiologically based modeling approach has been used to model the absorption of a lipophilic BCS Class II compound predominantly metabolized by CYP3A4, and to assess the interplay of absorption related parameters with the drug–drug interaction (DDI) potential.

Mechanistic Understanding of Brain Drug Disposition to Optimize the Selection of Potential Neurotherapeutics in Drug Discovery

ABSTRACTPurposeThe current project was undertaken with the aim to propose and test an in-depth integrative analysis of neuropharmacokinetic (neuroPK) properties of new chemical entities (NCEs), thereby optimizing the routine of evaluation and selection of novel neurotherapeutics.MethodsForty compounds covering a wide range of physicochemical properties and various CNS targets were investigated. The combinatory mapping approach was used for the assessment of the extent of blood-brain and cellular barriers transport via estimation of unbound-compound brain (Kp,uu,brain) and cell (Kp,uu,cell) partitioning coefficients. Intra-brain distribution was evaluated using the brain slice method. Intra- and sub-cellular distribution was estimated via calculation of unbound-drug cytosolic and lysosomal partitioning coefficients.ResultsAssessment of Kp,uu,brain revealed extensive variability in the brain penetration properties across compounds, with a prevalence of compounds actively effluxed at the blood-brain barrier. Kp,uu,cell was valuable for identification of compounds with a tendency to accumulate intracellularly. Prediction of cytosolic and lysosomal partitioning provided insight into the subcellular accumulation. Integration of the neuroPK parameters with pharmacodynamic readouts demonstrated the value of the proposed approach in the evaluation of target engagement and NCE selection.ConclusionsWith the rather easily-performed combinatory mapping approach, it was possible to provide quantitative information supporting the decision making in the drug discovery setting.

Molecular properties determining unbound intracellular and extracellular brain exposure of CNS drug candidates

In the present work we sought to gain a mechanistic understanding of the physicochemical properties that influence the transport of unbound drug across the blood-brain barrier (BBB) as well as the intra- and extracellular drug exposure in the brain. Interpretable molecular descriptors that significantly contribute to the three key neuropharmacokinetic properties related to BBB drug transport (Kp,uu,brain), intracellular accumulation (Kp,uu,cell), and binding and distribution in the brain (Vu,brain) for a set of 40 compounds were identified using partial least-squares (PLS) analysis. The tailoring of drug properties for improved brain exposure includes decreasing the polarity and/or hydrogen bonding capacity. The design of CNS drug candidates with intracellular targets may benefit from an increase in basicity and/or the number of hydrogen bond donors. Applying this knowledge in drug discovery chemistry programs will allow designing compounds with more desirable CNS pharmacokinetic properties.

Best practices for the use of itraconazole as a replacement for ketoconazole in drug–drug interaction studies

Ketoconazole has been widely used as a strong cytochrome P450 (CYP) 3A (CYP3A) inhibitor in drug–drug interaction (DDI) studies. However, the US Food and Drug Administration has recommended limiting the use of ketoconazole to cases in which no alternative therapies exist, and the European Medicines Agency has recommended the suspension of its marketing authorizations because of the potential for serious safety concerns. In this review, the Innovation and Quality in Pharmaceutical Developments Clinical Pharmacology Leadership Group (CPLG) provides a compelling rationale for the use of itraconazole as a replacement for ketoconazole in clinical DDI studies and provides recommendations on the best practices for the use of itraconazole in such studies. Various factors considered in the recommendations include the choice of itraconazole dosage form, administration in the fasted or fed state, the dose and duration of itraconazole administration, the timing of substrate and itraconazole coadministration, and measurement of itraconazole and metabolite plasma concentrations, among others. The CPLGs recommendations are based on careful review of available literature and internal industry experiences.

Pharmacokinetics of topiramate in patients with renal impairment, end-stage renal disease undergoing hemodialysis, or hepatic impairment

PURPOSE Topiramate is primarily renally excreted. Chronic renal and hepatic impairment can affect the clearance of topiramate. Therefore, the objective was to establish dosage guidelines for topiramate in chronic renal impairment, end-stage renal disease (ESRD) undergoing hemodialysis, or chronic hepatic impairment patients. METHODS In 3 separate open-label, parallel group studies (n=5-7/group), in patients with mild-moderate and severe renal impairment (based on creatinine clearance), ESRD requiring hemodialysis, or moderate-severe hepatic impairment (based on Child-Pugh classification) and matching healthy participants, pharmacokinetics of a single oral 100mg topiramate was determined. RESULTS Compared with healthy controls, overall exposure (AUC0-∞) for topiramate was higher in mild-moderate (85%) and severe renal impairment (117%), consistent with significantly (p<0.05) lower apparent total body clearance (CL/F) and renal clearance (CLR), leading to longer elimination half-life. Both CLR and CL/F of topiramate correlated well with renal function. CL/F was comparable in ESRD and severe renal impairment. Half of usual starting and maintenance dose is recommended in moderate-severe renal impairment patients, and those with ESRD. Hemodialysis effectively removed plasma topiramate with mean dialysis clearance approximately 12-fold greater than CL/F (123.5 mL/min versus 10.8 mL/min). Compared with healthy matched, patients with moderate-severe hepatic impairment exhibited small increase (29%) in topiramate peak plasma concentrations and AUC0-∞ values, consistent with lower CL/F (26%). Topiramate was generally well tolerated. CONCLUSION Half of usual dose is recommended for moderate-severe renal impairment and ESRD. Supplemental dose may be required during hemodialysis. Dose adjustments might not be required in moderate-severe hepatic impairments; however, the small sample size limits generalization.

Pharmacokinetic interactions between topiramate and pioglitazone and metformin

OBJECTIVE To investigate potential drug-drug interactions between topiramate and metformin and pioglitazone at steady state. METHODS Two open-label studies were performed in healthy adult men and women. In Study 1, eligible participants were given metformin alone for 3 days (500 mg twice daily [BID]) followed by concomitant metformin and topiramate (titrated to 100mg BID) from days 4 to 10. In Study 2, eligible participants were randomly assigned to treatment with pioglitazone 30 mg once daily (QD) alone for 8 days followed by concomitant pioglitazone and topiramate (titrated to 96 mg BID) from days 9 to 22 (Group 1) or to topiramate (titrated to 96 mg BID) alone for 11 days followed by concomitant pioglitazone 30 mg QD and topiramate 96 mg BID from days 12 to 22 (Group 2). An analysis of variance was used to evaluate differences in pharmacokinetics with and without concomitant treatment; 90% confidence intervals (CI) for the ratio of the geometric least squares mean (LSM) estimates for maximum plasma concentration (Cmax), area under concentration-time curve for dosing interval (AUC12 or AUC24), and oral clearance (CL/F) with and without concomitant treatment were used to assess a drug interaction. RESULTS A comparison to historical data suggested a modest increase in topiramate oral clearance when given concomitantly with metformin. Coadministration with topiramate reduced metformin oral clearance at steady state, resulting in a modest increase in systemic metformin exposure. Geometric LSM ratios and 90% CI for metformin CL/F and AUC12 were 80% (75%, 85%) and 125% (117%, 134%), respectively. Pioglitazone had no effect on topiramate pharmacokinetics at steady state. Concomitant topiramate resulted in decreased systemic exposure to pioglitazone and its active metabolites, with geometric LSM ratios and 90% CI for AUC24 of 85.0% (75.7%, 95.6%) for pioglitazone, 40.5% (36.8%, 44.6%) for M-III, and 83.8% (76.1%, 91.2%) for M-IV, respectively. This effect appeared more pronounced in women than in men. Coadministration of topiramate with metformin or pioglitazone was generally well tolerated by healthy participants in these studies. CONCLUSIONS A modest increase in metformin exposure and decrease in topiramate exposure was observed at steady state following coadministration of metformin 500 mg BID and topiramate 100mg BID. The clinical significance of the observed interaction is unclear but is not likely to require a dose adjustment of either agent. Pioglitazone 30 mg QD did not affect the pharmacokinetics of topiramate at steady state, while coadministration of topiramate 96 mg BID with pioglitazone decreased steady-state systemic exposure to pioglitazone, M-III, and M-IV. While the clinical consequence of this interaction is unknown, careful attention should be given to the routine monitoring for adequate glycemic control of patients receiving this concomitant therapy. Concomitant administration of topiramate with metformin or pioglitazone was generally well tolerated and no new safety concerns were observed.

Pharmacokinetic interactions between topiramate and diltiazem, hydrochlorothiazide, or propranolol

Drug–drug interactions between topiramate and diltiazem, hydrochlorothiazide, or propranolol were evaluated along with safety/tolerability in three open‐label studies. Healthy participants (aged 18–45 years) received topiramate 75 mg every 12 hours (q12h) and diltiazem 240 mg/day (study 1); topiramate 96 mg q12h and hydrochlorothiazide 25 mg/day (study 2); topiramate 100 mg q12h and propranolol 40–80 mg q12h (study 3). The pharmacokinetic parameters for topiramate, diltiazem (and active metabolites, desacetyldiltiazem [DEA], N‐demethyl diltiazem [DEM]), hydrochlorothiazide, and propranolol (and its active metabolite) were assessed at steady state. Results showed no effect of diltiazem on topiramate pharmacokinetics. However, a modest reduction in systemic exposures of diltiazem and DEA (10–27%) occurred during coadministration with topiramate. Systemic exposure of DEM was unaffected. Furthermore, oral and renal clearance of topiramate decreased (22–30%) significantly (P < 0.05) during coadministration with hydrochlorothiazide, while systemic exposure increased by 27–29%. Topiramate had no effect on hydrochlorothiazide pharmacokinetics. The results demonstrated lack of pharmacokinetic interaction between topiramate and propranolol. Overall, no new safety concerns emerged when topiramate was coadministered with diltiazem, hydrochlorothiazide, or propranolol.

Development of a Target cell-Biologics-Effector cell (TBE) complex-based cell killing model to characterize target cell depletion by T cell redirecting bispecific agents

ABSTRACT T-cell redirecting bispecific antibodies (bsAbs) or antibody-derived agents that combine tumor antigen recognition with CD3-mediated T cell recruitment are highly potent tumor-killing molecules. Despite the tremendous progress achieved in the last decade, development of such bsAbs still faces many challenges. This work aimed to develop a mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling framework that can be used to assist the development of T-cell redirecting bsAbs. A Target cell-Biologics-Effector cell (TBE) complex-based cell killing model was developed using in vitro and in vivo data, which incorporates information on binding affinities of bsAbs to CD3 and target receptors, expression levels of CD3 and target receptors, concentrations of effector and target cells, as well as respective physiological parameters. This TBE model can simultaneously evaluate the effect of multiple system-specific and drug-specific factors on the T-cell redirecting bsAb exposure–response relationship on a physiological basis; it reasonably captured multiple reported in vitro cytotoxicity data, and successfully predicted the effect of some key factors on in vitro cytotoxicity assays and the efficacious dose of blinatumomab in humans. The mechanistic nature of this model uniquely positions it as a knowledge-based platform that can be readily expanded to guide target selection, drug design, candidate selection and clinical dosing regimen projection, and thus support the overall discovery and development of T-cell redirecting bsAbs.

Clinical pharmacology, creating current and future success in drug development

In response to an accelerating emergence of novel therapeutic platforms, regulatory development paradigms, and advances in analytical technology, the Clinical Pharmacology Leadership Group within the International Consortium for Innovation and Quality in Drug Development convened a Working Group to discuss these matters and formulate a vision of clinical pharmacology science for the next decade. The Working Group met throughout 2013/2014 and identified a number of critical needs and opportunities that, if addressed, will ensure that clinical pharmacology continues to provide core value to the drug development process. This Working Group did recognize prior commentaries on the state of clinical pharmacology and considered those expert opinions during the course of our discussions, such as those authored by Rawlins, Honig, and LaLonde. In contrast with these earlier commentaries, this effort intended to identify immediate and long-term opportunities and present solutions that are particularly related to drug development efforts. Over the past decade, there have been remarkable advances in the field of molecular biology that, with increased understanding of disease etiology, have resulted in the transition of increasing numbers of novel therapeutic classes into clinical development. Antibody–drug conjugates, immune therapy directed at oncology and inflammation targets, synthetic DNA engaging mRNA (antisense), proteosome modulation, and gene editing are just a few of the emerging therapeutic classes and treatment modalities that have benefited from these advances with several that have achieved approval and others either in or close to entering clinical development. Likewise, a number of nascent analytical technologies have matured into viable means for measuring drug/ metabolite concentration in the blood compartment and in some cases at the site of action. Biomarkers that demonstrate ligand:receptor interaction (target engagement) and pharmacological activity are becoming commonplace in many therapeutic areas. Although oncology has used imaging technology tomonitor target engagement andeffectwith great success, similar value is being realized in other therapeutic areas such as neurology and cardiology. The evolution of companion diagnostics has occurred in concert and will grow commensurate with our ability to differentiate both patients and disease. These advances have given us significant opportunities to move promising therapies into pivotal clinical trials with a better understanding of the likelihood of technical success and associated risks regarding safety and efficacy. The discipline of clinical pharmacology plays a pivotal role in optimizing novel therapeutic approaches while ensuring that development decisions are made based on an understanding of inherent likelihood of success and attendant risks.

P.1.g.052 Understanding blood-brain barrier penetration and link to target engagement

V. Sinha, I. Loryan, P. De Boer, X. Langlois, C. Mackie, A. Van Peer, W. Drinkenburg, A. Vermeulen, D. Heald, M. Hammarlund-Udenaes Janssen Research and Development, Clinical Pharmacology, Beerse, Belgium Uppsala University, Translational PKPD Group Department of Pharmaceutical Biosciences, Uppsala, Sweden Janssen Research and Development, Experimental Medicine, Beerse, Belgium Janssen Research and Development, Neurosciences, Beerse, Belgium Janssen Research and Development, Pharmaceutical Development and Manufacturing Sciences, Beerse, Belgium Janssen Research and Development, Model Based Drug Development, Beerse, Belgium Janssen Research and Development, Clinical Pharmacology, Titusville, USA