Coformulation of a Novel Human α-Galactosidase A With the Pharmacological Chaperone AT1001 Leads to Improved Substrate Reduction in Fabry Mice.

Fabry disease is an X-linked lysosomal storage disorder caused by mutations in the gene that encodes α-galactosidase A and is characterized by pathological accumulation of globotriaosylceramide and globotriaosylsphingosine. Earlier, the authors demonstrated that oral coadministration of the pharmacological chaperone AT1001 (migalastat HCl; 1-deoxygalactonojirimycin HCl) prior to intravenous administration of enzyme replacement therapy improved the pharmacological properties of the enzyme. In this study, the authors investigated the effects of coformulating AT1001 with a proprietary recombinant human α-galactosidase A (ATB100) into a single intravenous formulation. AT1001 increased the physical stability and reduced aggregation of ATB100 at neutral pH in vitro, and increased the potency for ATB100-mediated globotriaosylceramide reduction in cultured Fabry fibroblasts. In Fabry mice, AT1001 coformulation increased the total exposure of active enzyme, and increased ATB100 levels in cardiomyocytes, cardiac vascular endothelial cells, renal distal tubular epithelial cells, and glomerular cells, cell types that do not show substantial uptake with enzyme replacement therapy alone. Notably, AT1001 coformulation also leads to greater tissue globotriaosylceramide reduction when compared with ATB100 alone, which was positively correlated with reductions in plasma globotriaosylsphingosine. Collectively, these data indicate that intravenous administration of ATB100 coformulated with AT1001 may provide an improved therapy for Fabry disease and thus warrants further investigation.

Impact of physiologically based pharmacokinetic modeling and simulation in drug development.

Physiologically based pharmacokinetic modeling and simulation can be used to predict the pharmacokinetics of drugs in human populations and to explore the effects of varying physiologic parameters that result from aging, ethnicity, or disease. In addition, the effects of concomitant medications on drug exposure can be investigated; prediction of the magnitude of drug interactions can impact regulatory communications or internal decision-making regarding the requirement for a clinical drug interaction study. Modeling and simulation can also help to inform the design and timings of clinical drug interaction studies, resulting in more efficient use of limited resources and improved planning in addition to promoting mechanistic understanding of observed drug interactions. These approaches have been used in GlaxoSmithKline from drug discovery to registration and have been applied to 41 drugs from a number of therapeutic areas. This report highlights the variety of questions that can be addressed by prospective or retrospective application of modeling and simulation and the impact this can have on clinical drug development (from candidate selection through clinical development to regulatory submissions).

Utilizing drug-drug interaction prediction tools during drug development: enhanced decision making based on clinical risk.

Several reports in the literature present the utility and value of in vitro drug-metabolizing enzyme inhibition data to predict in vivo drug-drug interactions in humans. A retrospective analysis has been conducted for 26 GlaxoSmithKline (GSK) drugs and drug candidates for which in vitro inhibition parameters have been determined, and clinical drug interaction information, from a total of 46 studies, is available. The dataset, for drugs with a diverse range of physiochemical properties, included both reversible and potentially irreversible cytochrome P450 inhibitors for which in vitro inhibition parameters (IC(50) or K(I)/k(inact) as appropriate) were determined using standardized methodologies. Mechanistic static models that differentiated reversible and metabolism-dependent inhibition, and also considered the contribution of intestinal metabolism for CYP3A4 substrates, were applied to estimate the magnitude of the interactions. Several pharmacokinetic parameters, including total C(max), unbound C(max), as well as estimates of hepatic inlet and liver concentration, were used as surrogates for the inhibitor concentration at the enzyme active site. The results suggest that estimated unbound liver concentration or unbound hepatic inlet concentration, with consideration of intestinal contribution, offered the most accurate predictions of drug-drug interactions (occurrence and magnitude) for the drugs in this dataset. When used with epidemiological information on comedication profiles for a given therapeutic area, these analyses offer a quantitative risk assessment strategy to inform the necessity of excluding specific comedications in early clinical studies and the ultimate requirement for clinical drug-drug interaction studies. This strategy has significantly reduced the number of clinical drug interaction studies performed at GSK.