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Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity—enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors., While drugs can interact in both target and off-target cell types, more favorable interaction in the target cell may nevertheless allow for a therapeutic window. Here, the authors show, using two yeast species as a model, that differential drug interactions indeed adjust the selective window.

Given the large number of possible drug combinations, computational methods to prioritize the most effective treatments are critical. Here, we present four methodologies for predicting synergistic drug interactions. Mechanism based synergy prediction utilizes well-characterized biological data to predict drug interactions based on drug target interactions. Guilt by association methods predict novel interactions based on similarity to compounds with known interactions. A frequentist approach uses a drug's known tendency to exhibit drug interactions. Compound descriptor array based methods use machine learning approaches to relate compound interactions with arrays of observations regarding a compound. The increasing success of drug synergy prediction methods offer a means toward designing rational drug combinations.

Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.

The search for high-affinity aptamers for targets such as proteins, small molecules, or cancer cells remains a formidable endeavor. Systematic Evolution of Ligands by EXponential Enrichment (SELEX) offers an iterative process to discover these aptamers through evolutionary selection of high-affinity candidates from a highly diverse random pool. This randomness dictates an unknown population distribution of fitness parameters, encoded by the binding affinities, toward SELEX targets. Adding to this uncertainty, repeating SELEX under identical conditions may lead to variable outcomes. These uncertainties pose a challenge when tuning selection pressures to isolate high-affinity ligands. Here, we present a novel stochastic hybrid model that describes the evolutionary selection of aptamers in order to explore the impact of these unknowns. To our surprise, we find that even single copies of high-affinity ligands in a pool of billions can strongly influence population dynamics, yet their survival is highly dependent on chance. We perform Monte Carlo simulations to explore the impact of environmental parameters, such as the target concentration, on selection efficiency in SELEX and identify new strategies to control these uncertainties to ultimately improve the outcome and speed of this time- and resource-intensive process., 6 pages, 8 figures, supporting information on the journal webpage

Poor-quality medicines undermine the treatment of infectious diseases, such as tuberculosis, which require months of treatment with rifampin and other drugs. Rifampin resistance is a critical concern for tuberculosis treatment. While subtherapeutic doses of medicine are known to select for antibiotic resistance, the effect of drug degradation products on the evolution of resistance is unknown. Here, we demonstrate that substandard drugs that contain degraded active pharmaceutical ingredients select for gene alterations that confer resistance to standard drugs. We generated drug-resistant Escherichia coli and Mycobacterium smegmatis strains by serially culturing bacteria in the presence of the rifampin degradation product rifampin quinone. We conducted Sanger sequencing to identify mutations in rifampin-resistant populations. Strains resistant to rifampin quinone developed cross-resistance to the standard drug rifampin, with some populations showing no growth inhibition at maximum concentrations of rifampin. Sequencing of the rifampin quinone-treated strains indicated that they acquired mutations in the DNA-dependent RNA polymerase B subunit. These mutations were localized in the rifampin resistance-determining region (RRDR), consistent with other reports of rifampin-resistant E. coli and mycobacteria. Rifampin quinone-treated mycobacteria also had cross-resistance to other rifamycin class drugs, including rifabutin and rifapentine. Our results strongly suggest that substandard drugs not only hinder individual patient outcomes but also restrict future treatment options by actively contributing to the development of resistance to standard medicines.

Drug interaction analysis, which reports the extent to which the presence of one drug affects the efficacy of another, is a powerful tool to select potent combinatorial therapies and predict connectivity between cellular components. Combinatorial effects of drug pairs often vary even for drugs with similar mechanism of actions. Therefore, drug interaction fingerprinting may be harnessed to differentiate drug identities. We developed a method to analyze drug interactions for the application of identifying active pharmaceutical ingredients, an essential step to assess drug quality. We developed a novel approach towards the identification of active pharmaceutical ingredients by comparing drug interaction fingerprint similarity metrics such as correlation and Euclidean distance. To expedite this method, we used bioluminescent E. coli in a simplified checkerboard assay to generate unique drug interaction fingerprints of antimicrobial drugs. Of 30 antibiotics studied, 29 could be identified based on their drug interaction fingerprints. We present drug interaction fingerprint analysis as a cheap, sensitive and quantitative method towards substandard and counterfeit drug detection.

Whether intrauterine exposures to alcohol, tobacco, marijuana, or cocaine predispose offspring to substance use in adolescence has not been established. We followed a sample of 149 primarily African American/African Caribbean, urban adolescents, recruited at term birth, until age 16 to investigate intrauterine cocaine exposure (IUCE). We found that in Kaplan-Meier analyses higher levels of IUCE were associated with a greater likelihood of initiation of any substance (licit or illicit), as well as marijuana and alcohol specifically. Adolescent initiation of other illicit drugs and cigarettes were analyzed only in the "any" summary variable since they were used too infrequently to analyze as individual outcomes. In Cox proportional hazard models controlling for intrauterine exposure to alcohol, tobacco, and marijuana and demographic and post-natal covariates, those who experienced heavier IUCE had a greater likelihood of initiation of any substance, and those with lighter intrauterine marijuana exposure had a greater likelihood of initiation of any substance as well as of marijuana specifically. Time-dependent higher levels of exposure to violence between ages of 8 and 16 were also robustly associated with initiation of any licit or illicit substance, and of marijuana, and alcohol particularly.

While synergistic small molecule combinations are often sought for increased efficacy, the selectivity of antimicrobial compound combinations is also paramount to drug therapy. Using the yeast model organisms S. cerevisiae and C. albicans, we conducted in vitro checkerboard assays for drug interactions of all pairwise combinations of 12 antifungals. We assessed the concavity of isobolograms for growth inhibition as a metric for drug interactions. We found that drug interactions were significantly conserved between these species despite varied concentration-response relationships of single drugs. We developed a metric, the fractional selectivity index, which indicates the relative growth of two cell types in any given ratio of a drug combination. Based on this analysis, we found drug regimens with increased selectivity for growth inhibition of one yeast species relative to another. Our analysis suggests a framework for discovering fixed-dose drug combinations with increased efficacy and selectivity for specific pathogens.

SummaryOne drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound (“drug”) pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate.

International audience; Background: Bread wheat (Triticum aestivum L.) is one of the most important crops worldwide and its production faces pressing challenges, the solution of which demands genome information. However, the large, highly repetitive hexaploid wheat genome has been considered intractable to standard sequencing approaches. Therefore the International Wheat Genome Sequencing Consortium (IWGSC) proposes to map and sequence the genome on a chromosome-by-chromosome basis. Methodology/Principal Findings: We have constructed a physical map of the long arm of bread wheat chromosome 1A using chromosome-specific BAC libraries by High Information Content Fingerprinting (HICF). Two alternative methods (FPC and LTC) were used to assemble the fingerprints into a high-resolution physical map of the chromosome arm. A total of 365 molecular markers were added to the map, in addition to 1122 putative unique transcripts that were identified by microarray hybridization. The final map consists of 1180 FPC-based or 583 LTC-based contigs. Conclusions/Significance: The physical map presented here marks an important step forward in mapping of hexaploid bread wheat. The map is orders of magnitude more detailed than previously available maps of this chromosome, and the assignment of over a thousand putative expressed gene sequences to specific map locations will greatly assist future functional studies. This map will be an essential tool for future sequencing of and positional cloning within chromosome 1A.

Professional guidelines recommend annual screening, brief intervention, and referral to treatment (SBIRT) as part of health maintenance for all adolescents, but reported screening rates have been low and no report has documented the techniques being used. The objective of this study was to describe the results of a statewide questionnaire regarding adolescent substance use screening rates and techniques used by primary care physicians practicing in Massachusetts. A questionnaire was mailed to every licensed physician registered as practicing pediatrics ( N = 2176), family medicine ( N = 1335), or both ( N = 8) in the Massachusetts Board of Medicine database. After eliminating physicians who did not provide care for adolescents, the survey response rate was 28% and the final analyzable sample consisted of 743 surveys. Less than half of respondents reported using a validated adolescent screening tool. The majority of respondents used ineffective screening practices for adolescent substance use. Further physician training is recommended to encourage the use of developmentally appropriate screening tools and interventions for adolescents.

Background Laboratory drug testing programs may be effective in reducing substance use by adolescents, but developmentally appropriate programs have not been described, and it is unknown if adolescents would be willing to participate in drug testing. Objective To describe a drug testing protocol for adolescents and report on acceptance rate by patients participating in an outpatient adolescent substance abuse program. Methods Eligible adolescents participating in an outpatient substance abuse treatment program were offered a random laboratory drug testing program that is described in detail in this manuscript. We recorded whether they accepted and, if not, the reason for refusal. Results Of the first 114 eligible patients, 67 (59%) agreed to participate in a drug testing program (PDT). Conclusions A majority of adolescents participating in an outpatient drug treatment program agreed to participate in a drug testing program that requires frequent urine specimens and reports results to parents. Future studies should determine how this program affects treatment outcomes and whether this program is feasible in primary care.