PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC (PK) PROPERTIES USING QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIPS (QSPKR) AND INTERSPECIES PHARMACOKINETIC ALLOMETRIC SCALING (PK-AS) APPROACHES FOR FOUR DIFFERENT PHARMACOLOGICAL CLASSES OF COMPOUNDS

This research developed and validated QSPKR models for predicting in-vivo human, systemic biologically relevant PK properties (i.e., reflecting the disposition of the unbound drug) of four, preselected, pharmacological classes of drugs, namely, benzodiazepines (BZD), neuromuscular blocking agents (NMB), triptans (TRP) and class III antiarrhythmic agents (AAR), as well as PK allometric scaling (PK-AS) models for BZD and NMB, using pertinent human and animal systemic PK information (fu, CLtot, Vdss and fe) from published literature. Overall, lipophilicity (logD7.4) and molecular weight (MW) were found to be the most important and statistically significant molecular properties, affecting biologically relevant systemic PK properties, and the observed relationships were mechanistically plausible: For relatively small MW and lipophilic molecules, (e.g., BZD), an increase in logD7.4 was associated with a decrease in fu, an increase in Vdssu and CLnonrenu, suggesting the prevalence of nonspecific hydrophobic interactions with biological membranes/plasma proteins as well as hepatic partitioning/DME binding. Similar trends were observed in fu and Vdssu for intermediate to large MW, hydrophilic molecules (e.g., NMB). However, although similar trends were observed in fu and Vdssu for relatively hydrophilic, intermediate MW molecules (e.g., TRP), and a heterogeneous class (e.g., Class III AAR), logD7.4 and MW were found to be highly correlated, i.e., the indepdendent effects of logD7,4 and MW cannot be assessed NMB, TRP and Class III AAR show mechanistically diverse clearance pathways, e.g., hepatobiliary, extrahepatic, enzymatic/chemical degradation and renal excretion; therefore, effects of the logD7.4 and/or MW are note generalizable for any of the clearances across classes. PK-AS analyses showed that Vdssu and Vdss scaled well with body weight across animal species (including humans) for BZD. Overall, within the limitations of the methods (and the sample size), ‘acceptable’ predictions (i.e., within 0.5- to 2.0-fold error range) were obtained for Vdssu and Vdss for BZD (and fu correction resulted in improvement of the prediction); however, none of the CLtot predictions were acceptable, suggesting major, qualitative interspecies differences in drug metabolism, even after correcting for body weight (BW). NMB undergo little extravascular distribution owing to their relatively large MW and charged nature, and, as a result, a high percentage of acceptable predictions was obtained for Vdss (based on BW). Similarly, the prediction of CLren (based on BW and glomerular filtration rate, GFR) was acceptable, suggesting that NMB are cleared by GFR across species, and there are no interspecies differences in their tubular handling. On the other hand, CLtot (and/or CLnonren) could not be acceptably predicted by PK-AS, suggesting major differences in their clearance mechanisms across animal species

Modeling a Composite Score in Parkinson's Disease Using Item Response Theory

In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies

Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales

Purpose: The aim of this work was to allow combination of information from recent and historical trials in Parkinson's Disease (PD) by developing bridging methodology between two versions of the clinical endpoint. Methods: A previously developed Item Response Model (IRM), that described longitudinal changes in Movement Disorder Society (MDS) sponsored revision of Unified Parkinson's Disease Rating Scale (UPDRS) [MDS-UPDRS] data from the De Novo PD cohort in Parkinson's Progression Markers Initiative, was first adapted to describe baseline UPDRS data from two clinical trials, one in subjects with early PD and another in subjects with advanced PD. Assuming similar IRM structure, items of the UPDRS version were mapped to those in the MDS-UPDRS version. Subsequently, the longitudinal changes in the placebo arm of the advanced PD study were characterized. Results: The parameters reflecting differences in the shared items between endpoints were successfully estimated, and the model diagnostics indicated that mapping was better for early PD subjects (closer to De Novo cohort) than for advanced PD subjects. Disease progression for placebo in advanced PD patients was relatively shallow. Conclusion: An IRM able to handle two variants of clinical PD endpoints was developed; it can improve the utilization of data from diverse sources and diverse disease populations