The critical importance of membrane‐bound transporters in pharmacotherapy is widely recognized, but little is known about drug transporter activity in children. In this white paper, the Pediatric Transporter Working Group presents a systematic review of the ontogeny of clinically relevant membrane transporters (e.g., SLC, ABC superfamilies) in intestine, liver, and kidney. Different developmental patterns for individual transporters emerge, but much remains unknown. Recommendations to increase our understanding of membrane transporters in pediatric pharmacotherapy are presented.

No abstract

To alleviate the problems in the receptor-based design of metalloprotein ligands due to inadequacies in the force-field description of coordination bonds, a four-tier approach was devised. Representative ligand-metalloprotein interaction energies are obtained by subsequent application of (1) docking with metal-binding-guided selection of modes; (2) optimization of the ligand-metalloprotein complex geometry by combined quantum mechanics and molecular mechanics (QM/MM) methods; (3) conformational sampling of the complex with constrained metal bonds by force-field based molecular dynamics (MD); and (4) a single point QM/MM energy calculation for the time-averaged structures. The QM/MM interaction energies are, in a linear combination with the desolvation-characterizing changes in the solvent-accessible surface areas, correlated with experimental data. The approach was applied to structural correlation of published binding free energies of a diverse set of 28 hydroxamate inhibitors to zinc-dependent matrix metalloproteinase 9 (MMP-9). Inclusion of step 3 and step 4 significantly improved both correlation and prediction. The two descriptors explained 90% of variance in inhibition constants of all 28 inhibitors, ranging from 0.08 to 349 nM, with the average unassigned error of 0.318 log units. The structural and energetic information obtained from the timeaveraged MD simulation results helped understand the differences in binding modes of related compounds.

Abstract. Our knowledge of the major mechanisms underlying the effect of food on drug absorption allows reliable qualitative prediction based on biopharmaceutical properties, which can be assessed during the pre-clinical phase of drug discovery. Furthermore, several recent examples have shown that physiologically based absorption models incorporating biorelevant drug solubility measurements can provide quite accurate quantitative prediction of food effect. However, many molecules currently in development have distinctly sub-optimal biopharmaceutical properties, making the quantitative prediction of food effect for different formulations from in vitro data very challenging. If such drugs reach clinical development and show undesirable variability when dosed with food, improved formulation can help to reduce the food effect and carefully designed in vivo studies in dogs can be a useful guide to clinical formulation development. Even so, such in vivo studies provide limited throughput for screening, and food effects seen in dog cannot always be directly translated to human. This paper describes how physiologically based absorption modeling can play a role in the prediction of food effect by integrating the data generated during pre-clinical and clinical research and development. Such data include physicochemical and in vitro drug properties, biorelevant solubility and dissolution, and in vivo pre-clinical and clinical pharmacokinetic data. Some background to current physiological absorption models of human and dog is given, and refinements to models of in vivo drug solubility and dissolution are described. These are illustrated with examples using GastroPlus™ to simulate the food effect in dog and human for different formulations of two marketed drugs.

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-inhuman pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-inhuman physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-inhuman prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Tissue components hydrolyzing matrix metalloproteinases (MMPs) exhibit a high sequence similarity (56 -64% in catalytic domains) and yet a significant degree of functional specificity. The hexapeptide-binding sites of 24 known human MMPs were compared in terms of their force field interaction energies with five probes that are most frequently encountered in substrates and inhibitors. The probes moved along a grid enclosing partially flexible binding sites in rigid catalytic domains that were represented by published experimental structures and comparative models and new comparative models for nine most recently characterized MMPs. For individual MMPs, representative interaction energies were obtained as averages for all suitable experimental structures. Correlations of the representative energies for all MMP pairs were succinctly catalogued for individual probes, subsites, and correlation levels. Among the probes (neutral sp 3 carbon and sp 3 oxygen, positive sp 3 nitrogen and hydrogen, and negative carbonyl oxygen), the last probe is least distinctive. Similarities of subsites are decreasing as S1 > S2 > S3 > S1 ϳ S3 > S2 . Most interesting, occupancies of subsites in published structures of MMP-inhibitor complexes follow an almost parallel trend, alluding to overall low selectivity of known MMP inhibitors. Flexible subsite S1 that appears as the specificity pocket in rigid x-ray structures is actually very similar among individual MMPs. Several correlations indicated that MMPs 3, 8, and 12 have similar binding sites. Modeling results are corroborated with published experimental data on MMP inhibition and substrate specificities. The results provide numerous clues for development of specific inhibitors and substrates, as well as for selection of MMPs for testing that provides maximum information without redundant experiments.

The Biopharmaceutics Classification System (BCS) allows compounds to be classified based on their in vitro solubility and intestinal permeability. The BCS has found widespread use in the pharmaceutical community as an enabling guide for the rational selection of compounds, formulation for clinical advancement and generic biowaivers. The Pediatric Biopharmaceutics Classification System (PBCS) working group was convened to consider the possibility of developing an analogous pediatric based classification system. Since there are distinct developmental differences that can alter intestinal contents, volumes, permeability and potentially biorelevant solubilities at the different ages, the PBCS working group focused on identifying age specific issues that would need to be considered in establishing a flexible, yet rigorous PBCS. Objective To summarize the findings of the PBCS working group and provide insights into considerations required for the development of a pediatric based biopharmaceutics classification system. Methods Through several meetings conducted both at The Eunice Kennedy Shriver National Institute of Child Health, Human Development (NICHD)-US Pediatric Formulation Initiative (PFI) workshop (November 2011) and via teleconferences, the PBCS working group considered several high level questions that were raised to frame the classification system. In addition, the PBCS working group identified a number of knowledge gaps that would need to be addressed in order to develop a rigorous PBCS. Results It was determined that for a PBCS to be truly meaningful, it would need to be broken down into several different age groups that would account for developmental changes in intestinal permeability, luminal contents, and gastrointestinal transit. Several critical knowledge gaps where identified including: 1) a lack of fully understanding the ontogeny of drug metabolizing enzymes and transporters along the gastrointestinal (GI) tract, in the liver and in the kidney; 2) an incomplete understanding of age-based changes in the GI, liver and kidney physiology; 3) a clear need to better understand age-based intestinal permeability and fraction absorbed required to develop the PBCS; 4) a clear need for the development and organization of pediatric tissue biobanks to serve as a source for ontogenic research; and 5) a lack of literature published in age-based pediatric pharmacokinetics in order to build Physiologically- and Population-Based Pharmacokinetic (PBPK) databases. Conclusions To begin the process of establishing a PBPK model, ten pediatric therapeutic agents were selected (based on their adult BCS classifications). Those agents should be targeted for additional research in the future. The PBCS working group also identified several areas where a greater emphasis on research is needed to enable the development of a PBCS.

Receptor site modeling methods usually use one binding mode (conformation and/or orientation) for each ligand in a 1:1 complex with receptor. Multiple modes should be considered instead because (1). they have frequently been observed experimentally; (2). in a series, ligands can bind in single yet different modes; and (3). a series may only exhibit one but unknown mode and a few plausible modes must be examined. For multimode binding, the observed ligand/receptor association constant is the sum of the association constants that characterize individual binding modes. This relation, when applied to Comparative Molecular Field Analysis (CoMFA), results in a dependence of the observed binding energy on the probe energies that is nonlinear in optimized parameters. The dependence was linearized to allow parameter optimization by the partial least-squares method that was used iteratively until self-consistency. In addition to the standard CoMFA output, the procedure objectively selects one or a few optimal binding modes out of a dozen or more modes that are considered for each ligand. The approach was applied to published data for binding of 34 polychlorinated dibenzofurans to the aryl hydrocarbon receptor. Descriptive and predictive abilities of the 16-mode model were significantly better than for the one-, two-, and four-mode models. Predominantly, edge-aligned modes were selected that are seldom used in CoMFA. Since inclusion of multimode binding only changes the form of the correlation equation and does not affect the number of optimized parameters, the improvement is believed to be due to a more realistic description.