Multidrug resistant pulmonary tuberculosis treatment regimens and patient outcomes: an individual patient data meta-analysis of 9,153 patients.

Treatment of multidrug resistant tuberculosis (MDR-TB) is lengthy, toxic, expensive, and has generally poor outcomes. We undertook an individual patient data meta-analysis to assess the impact on outcomes of the type, number, and duration of drugs used to treat MDR-TB

Culture Conversion Among HIV Co-Infected Multidrug-Resistant Tuberculosis Patients in Tugela Ferry, South Africa

Little is known about the time to sputum culture conversion in MDR-TB patients co-infected with HIV, although such patients have, historically, had poor outcomes. We describe culture conversion rates among MDR-TB patients with and without HIV-co-infection in a TB-endemic, high-HIV prevalent, resource-limited setting.Patients with culture-proven MDR-TB were treated with a standardized second-line regimen. Sputum cultures were taken monthly and conversion was defined as two negative cultures taken at least one month apart. Time-to-conversion was measured from the day of initiation of MDR-TB therapy. Subjects with HIV received antiretroviral therapy (ART) regardless of CD4 count.Among 45 MDR-TB patients, 36 (80%) were HIV-co-infected. Overall, 40 (89%) of the 45 patients culture-converted within the first six months and there was no difference in the proportion who converted based on HIV status. Median time-to-conversion was 62 days (IQR 48-111). Among the five patients who did not culture convert, three died, one was transferred to another facility, and one refused further treatment before completing 6 months of therapy. Thus, no patients remained persistently culture-positive at 6 months of therapy.With concurrent second-line TB and ART medications, MDR-TB/HIV co-infected patients can achieve culture conversion rates and times similar to those reported from HIV-negative patients worldwide. Future studies are needed to examine whether similar cure rates are achieved at the end of MDR-TB treatment and to determine the optimal use and timing of ART in the setting of MDR-TB treatment

Machine Learning For Drug Development: Integrating Genomic, Chemical, And Clinical Data To Identify Drug Targets, Efficacies, Adverse Events, And Combinations

Despite recent technological advances, drug development has remained a challenging and inefficient process. Machine learning methods have the potential to accelerate this process by using information from past drug successes and failures to decipher the mechanisms and activities of new compounds. This will become even more crucial in the age of “precision medicine” where thorough mechanistic knowledge will be needed to properly position compounds. The purpose of this dissertation is to address this through the development of methods for drug target identification, biomarker identification, indication selection, and adverse event prediction. First we introduce BANDIT to accelerate the process of drug target identification/deconvolution. BANDIT integrates multiple different data types within a Bayesian network to predict the targets for both new and approved small molecules. We found that BANDIT was able to accurately recover a large number of known drug-target interactions, identify new drugs for a common cancer target, and identify DRD2 as the target for ONC201 – a first-in-class molecule in clinical development. Our work on ONC201 led us to ask how we could integrate known information on DRD2 with gene expression profiling and BANDIT to better select analogs and indications for ONC201. We found that we could accurately rank analogs based on measured efficacy, select new cancer types where ONC201 was likely to be efficacious, and identified DRD5 and cancer stem cell genes as biomarkers for ONC201 activity. Following our work on ONC201 and drug target identification, we asked whether these methods could be applied to predict specific adverse events for a specific drug. Building off previous work published by our lab, we developed MAESTER, a data-driven machine learning approach that integrates properties on a compound’s structure and targets, with tissue wide gene expression profiling and known biological networks to calculate the probability of a compound presenting with a set of tissue specific adverse events in the clinic. We found that MAESTER could accurately identify known side effects of approved drugs and could even pinpoint the adverse events of drugs that were approved and later withdrawn for tissue specific toxicities. Altogether this work demonstrates how challenging problems in drug development could be addressed through the integration of diverse datasets. These approaches have the potential to transform the current drug development pipeline by focusing experimental efforts, and identifying new compounds with therapeutic potential, and choosing optimal indication and patient populations – all which could have a direct impact on patient care

Organization of Enzyme Concentration across the Metabolic Network in Cancer Cells

<div><p>Rapid advances in mass spectrometry have allowed for estimates of absolute concentrations across entire proteomes, permitting the interrogation of many important biological questions. Here, we focus on a quantitative aspect of human cancer cell metabolism that has been limited by a paucity of available data on the abundance of metabolic enzymes. We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network. At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome. We then use this analysis to investigate several outstanding problems in cancer metabolism, including the diversion of glycolytic flux for biosynthesis, the relative contribution of nitrogen assimilating pathways, and the origin of cellular redox potential. We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding. Together our results demonstrate that a relatively simple analysis of the abundance of metabolic enzymes was able to reveal many insights into the organization of the human cancer cell metabolic network.</p></div

A machine learning and network framework to discover new indications for small molecules.

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts

Distribution of metabolic proteome percentage across all cell lines for various pathways.

<p>For each distribution the median, standard deviations, max, and min are indicated. Inset contains a magnified look at the pathways with the highest percentages of the metabolic proteome.</p

Global profile of glycolytic enzyme concentrations across all cell lines.

<p><b>(a)</b> Distribution of cell protein copy (CPC) values for all glycolytic/gluconeogenic proteins across all cell lines. For each protein the median, standard deviations, max, and min are indicated. <b>(b)</b> Probability distribution function of CPC values for all metabolic proteins and the glycolytic/gluconeogenic subset—different subsets are denoted by different colors. Values were binned with a bin difference of 10^0.2 and the relative frequency, or percentage of values falling into that bin, were plotted. <b>(c)</b> Distribution of 11 glycolytic proteins in a sequential pathway order. Center dot indicates average CPC value for each protein with bars indicating the max and min measurements across all cell lines. <b>(d)</b> Pathway diagram of glycolysis activity. Blue squares indicate branching into other biological pathways, blue hexagons indicate intermediate metabolites, and purple circles indicate reacting enzymes. Size of purple circle is proportional to the average CPC value for that enzyme across all cell lines.</p

Cofactor and protein concentration analysis within the human metabolic protein network.

<p>Network diagram of glycolysis illustrating the abundance of aminotransferases and enzymes that utilize NAD(P)/NAD(P)H as a cofactor. The size of the nodes corresponds to the average abundance of the proteins.</p

Branch point analysis across the human metabolic protein network.

<p><b>(a)</b> Diagram clarifying method for computing various Branch Divergence scores in 2 different circumstances. Orange squares indicate reactant and product metabolites with blue ovals indicating the reacting enzymes. <b>(b)</b> Histogram of Branch Divergence Scores based on top 2 values. Each bin is lower end inclusive with a bin size of 0.1. <b>(c)</b> Histogram of Branch Divergence Scores based on top 3 values. Each bin is lower end inclusive with a bin size of 0.1. <b>(d)</b> Plot indicating the p-values for pathways that have significantly different counts in one-sided and equally distributed pathways (defined as a p-value < 0.05). <b>(e)</b> Ratio of one-sided counts to equally distributed counts for significant pathways.</p

Global intracellular distribution of metabolic protein concentrations across all cell lines.

<p><b>(a)</b> Pie chart diagraming the total percentage of metabolic proteins across all cell lines. Metabolic proteins are differentiated by a different color inset. <b>(b)</b> Probability distribution function of cell protein copy (CPC) values for all proteins and the metabolic subset—different subsets are denoted by different colors. Log₁₀ values were binned with a bin difference of 10^0.3 and the relative frequency, or percentage of values falling into that bin, were plotted.</p