Publications

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

The antiviral potency of the cytokine IFN-α has been long appreciated but remains poorly understood. A number of studies have suggested that induction of the apolipoprotein B mRNA editing enzyme, catalytic polypeptide 3 (APOBEC3) and bone marrow stromal cell antigen 2 (BST-2/tetherin/CD317) retroviral restriction factors underlies the IFN-α-mediated suppression of HIV-1 replication in vitro. We sought to characterize the as-yet-undefined relationship between IFN-α treatment, retroviral restriction factors, and HIV-1 in vivo. APOBEC3G, APOBEC3F, and BST-2 expression levels were measured in HIV/hepatitis C virus (HCV)-coinfected, antiretroviral therapy-naïve individuals before, during, and after pegylated IFN-α/ribavirin (IFN-α/riba) combination therapy. IFN-α/riba therapy decreased HIV-1 viral load by -0.921 (±0.858) log(10) copies/mL in HIV/HCV-coinfected patients. APOBEC3G/3F and BST-2 mRNA expression was significantly elevated during IFN-α/riba treatment in patient-derived CD4+ T cells (P < 0.04 and P < 0.008, paired Wilcoxon), and extent of BST-2 induction was correlated with reduction in HIV-1 viral load during treatment (P < 0.05, Pearson's r). APOBEC3 induction during treatment was correlated with degree of viral hypermutation (P < 0.03, Spearman's ρ), and evolution of the HIV-1 accessory protein viral protein U (Vpu) during IFN-α/riba treatment was suggestive of increased BST-2-mediated selection pressure. These data suggest that host restriction factors play a critical role in the antiretroviral capacity of IFN-α in vivo, and warrant investigation into therapeutic strategies that specifically enhance the expression of these intrinsic immune factors in HIV-1-infected individuals.

BACKGROUND

Human immunodeficiency virus (HIV) infection causes neurocognitive or motor function deficits in children with advanced disease, but it is unclear whether children with CD4 cell measures above the World Health Organization (WHO) thresholds for antiretroviral therapy (ART) initiation suffer significant impairment.

METHODS

The neurocognitive and motor functions of HIV-infected ART-naive Ugandan children aged 6-12 years with CD4 cell counts of >350 cells/μL and CD4 cell percentage of >15% were compared with those of HIV-uninfected children, using the Test of Variables of Attention (TOVA), the Kaufman Assessment Battery for Children, second edition (KABC-2), and the Bruininks-Oseretsky Test of Motor Proficiency, second edition (BOT-2).

RESULTS

Ninety-three HIV-infected children (median CD4 cell count, 655 cells/μL; plasma HIV RNA level, 4.7 log(10) copies/mL) were compared to 106 HIV-uninfected children. HIV-infected children performed worse on TOVA visual reaction times (multivariate analysis of covariance; P = .006); KABC-2 sequential processing (P = .005), simultaneous processing (P = .039), planning/reasoning (P = .023), and global performance (P = .024); and BOT-2 total motor proficiency (P = .003). High plasma HIV RNA level was associated with worse performance in 10 cognitive measures and 3 motor measures. In analysis of only WHO clinical stage 1 or 2 HIV-infected children (n = 68), significant differences between the HIV-infected and HIV-uninfected groups (P < .05) remained for KABC-2 sequential processing, KABC-2 planning/reasoning, and BOT-2 motor proficiency.

CONCLUSIONS

Significant motor and cognitive deficits were found in HIV-infected ART-naive Ugandan children with CD4 cell counts of ∼350 cells/μL and percentages of >15%. Study of whether early initiation of ART could prevent or reverse such deficits is needed.

Individuals with multimorbidity may be at increased risk of hospitalization and death. Comorbidity indexes do not capture severity of illness or healthcare utilization; however, inflammation biomarkers that are not disease-specific may predict hospitalization and death in older adults. We sought to predict hospitalization and mortality of older adults using inflammation biomarkers. From a prospective, observational study, 370 community-dwelling adults 65 years or older from central Alabama participated in an in-home assessment and provided fasting blood samples for inflammation biomarker testing in 2004. We calculated an inflammation summary score (range 0-4), one point each for low albumin, high C-reactive protein, low cholesterol, and high interleukin-6. Utilizing Cox proportional hazards models, inflammation summary scores were used to predicted time to hospitalization and death during a 4-year follow up period. The mean age was 73.7 (±5.9 yrs), and 53 (14%) participants had summary scores of 3 or 4. The rates of dying were significantly increased for participants with inflammation summary scores of 2, 3, or 4 (hazard ratio (HR) 2.22, 2.78, and 7.55, respectively; p<0.05). An inflammation summary score of 4 significantly predicted hospitalization (HR 5.92, p<0.05). Community-dwelling older adults with biomarkers positive for inflammation had increased rates of being hospitalized or dying during the follow up period. Assessment of the individual contribution of particular inflammation biomarkers in the prediction of health outcomes in older populations and the development of validated summary scores to predict morbidity and mortality are needed.