Crystallographic Analysis and Kinetic Studies of HIV-1 Protease and Drug-Resistant Mutants

HIV-1 protease is the most effective target for drugs to treat AIDS, however, the long-term therapeutic efficiency is restricted by the rapid development of drug resistant variants. To better understand the molecular basis of drug resistance, crystallographic and kinetic studies were applied to wild-type HIV-1 protease (PR) and drug-resistant mutants, PRV82A, and PRI84V, in complex with substrate analogues, the current drug saquinavir and the new inhibitor UIC-94017 (TMC-114). UIC-94017 was also studied with mutants PRD30N and PRI50V. The drug-resistant mutations V82A, I84V, D30N and I50V participate in substrate binding. Eighteen crystal structures were refined at resolutions of 0.97-1.60A. The high accuracy of the atomic resolution crystal structures helps understand the reaction mechanism of HIV-1 PR. Different binding modes are observed for different types of inhibitors. The substrate analogs have more extended interactions with PR subsites up to S5-S5\u27, while the clinical inhibitors maximize the contacts within S2-S2\u27. Hydrophobic interactions are the major force for saquinavir binding since it was designed with enhanced hydrophobic groups based on substrate side-chains. In contrast, the new clinical inhibitor UIC-94017 was designed to mimic the hydrogen bonds between substrates and PR. UIC-94017 forms polar interactions with the PR main-chain atoms of Asp29/30, which have been proposed to be critical for its potency against resistant HIV. The mutants showed different structural and kinetic effects, depending on the inhibitor and location of the mutations. The observed structural changes were consistent with the relative inhibition data. Both PRI84V and PRI50V lost favorable hydrophobic interactions with inhibitor compared with PR. Similarly, in PRD30N the UIC-94017 had a water-mediated interaction with the side-chain of Asn30 rather than the direct interaction observed in PR. However, PRV82A compensated for the mutation by shifts of the backbone of Ala82. Furthermore, the complexes of PRV82A showed smaller shifts relative to PR, but more movement of the peptide analog, compared to complexes with clinical inhibitors. The structures suggest that substrate analogs have more flexibility than the drugs to accommodate the structural changes caused by mutation, which may explain how HIV can develop drug resistance while retaining the ability of PR to hydrolyze natural substrates

Antiretroviral Regimens in HIV-Infected Adults Receiving Medical Care in the United States: Medical Monitoring Project, 2009

Effective antiretroviral therapy (ART) is essential for viral suppression (VS) in HIV-infected patients. However, there is a lack of nationally representative data on types of ART regimens used and their impact on VS. This thesis used self-reported interview and abstracted medical record from 2009 Medical Monitoring Project (MMP) to study ART regimen type and related health outcomes. Results showed that 88.6% of HIV-infected adults in care was prescribed ART, and about half took regimens designated as ‘preferred’ according to U.S ART guidelines. Among MMP participants prescribed ART, 62.7% achieved durable VS, 77.8% achieved recent VS, 83.5% were 100% dose-adherent, and 17.1% reported side effects. Multivariate regression analyses revealed that although ART was critical for VS, there were minor differences in health outcomes among the major ART classes in the U.S. ART guidelines or six most-commonly used regimens. This study could be potentially useful for future strategic planning of HIV care

Modeling Chemical Interaction Profiles: II. Molecular Docking, Spectral Data-Activity Relationship, and Structure-Activity Relationship Models for Potent and Weak Inhibitors of Cytochrome P450 CYP3A4 Isozyme

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D 13C-NMR and 1D 15N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Injection practices and sexual behaviors among persons with diagnosed HIV infection who inject drugs - United States, 2015-2017.

During 2016, 6% of persons in the United States who received a diagnosis of human immunodeficiency virus (HIV) infection had their HIV infection attributed to injection drug use (1). Injection practices and sexual behaviors among HIV-positive persons who inject drugs, such as injection equipment sharing and condomless sex, can increase HIV transmission risk; nationally representative estimates of the prevalences of these behaviors are lacking. The Medical Monitoring Project (MMP) is an annual, cross-sectional survey that reports nationally representative estimates of clinical and behavioral characteristics among U.S. adults with diagnosed HIV (2). CDC used MMP data to assess high-risk injection practices and sexual behaviors among HIV-positive persons who injected drugs during the preceding 12 months and compared their HIV transmission risk behaviors with those of HIV-positive persons who did not inject drugs. During 2015-2017, approximately 10% (weighted percentage estimate) of HIV-positive persons who injected drugs engaged in distributive injection equipment sharing (giving used equipment to another person for use); nonsterile syringe acquisition and unsafe disposal methods were common. Overall, among HIV-positive persons who injected drugs, 80% received no treatment, and 57% self-reported needing drug or alcohol treatment. Compared with HIV-positive persons who did not inject drugs, those who injected drugs were more likely to have a detectable viral load (48% versus 35%; p = 0.008) and engage in high-risk sexual behaviors (p<0.001). Focusing on interventions that reduce high-risk injection practices and sexual behaviors and increase rates of viral suppression might decrease HIV transmission risk among HIV-positive persons who inject drugs. Successful substance use treatment could also lower risk for transmission and overdose through reduced injection