The emergence of drug-resistant bacteria threatens to catapult humanity back again

The emergence of drug-resistant bacteria threatens to catapult humanity back again to the pre-antibiotic era. can accelerate the recognition of novel antibiotics in an academic setting leading to improved hit rates and faster transitions to pre-clinical and medical testing. The current review identifies two machine-learning techniques neural networks and decision trees that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. Introduction Addressing the threat of drug-resistant bacteria is one of modern medicine’s greatest challenges. The excitement surrounding Alexander Fleming’s discovery of penicillin in 1928 which has rightfully been described as a “turning point in history” (1) was quickly followed by the disheartening realization that bacteria can mount a counterassault. Penicillinase a β-lactamase capable of degrading penicillin Garcinol was identified even before penicillin had been applied clinically (2). Following widespread use in hospitals sulfonamide-resistant and penicillin-resistant emerged in the 1930s (3) and 1940s (4) respectively. Many other bacterial strains have subsequently developed resistance including some that are impervious to multiple antibiotics (1 5 In retrospect this development is hardly surprising. Humans use hundreds of thousands of tons of antibiotics per year (6) for medical veterinary and agricultural purposes (5) thereby applying tremendous anthropogenic evolutionary pressure that favors resistance. Many resistance-conferring bacterial proteins existed even before the medical Garcinol use of antibiotics (7) and novel mutations in modern times have produced additional resistance genes. To complicate matters further gene exchange often plasmid mediated (8) is a “universal property of bacteria” (1) that does not respect even taxonomic and ecological boundaries (5) allowing resistance to spread quickly. As a single example of this phenomenon consider the fact that 40-60% of nosocomial in the U.S. and U.K. is now methicillin-resistant (MRSA) and many strains are multi-drug resistant (MDR) (5). The economic and social burdens associated with Rabbit Polyclonal to CES2. treating resistant bacterial infections are substantial. Each year in Europe and the United States alone these contagions result in ~11 million additional hospital days and over $20 billion in additional health care costs (9 10 Europe reports ~400 0 annual MDR infections that result in 25 0 deaths (9). While the development of novel therapeutics might initially appear to be profitable given the magnitude of the threat in fact pharmaceutical companies have shied away from antibiotic development in recent years. New antibiotics are typically only used after more traditional medicines have failed. Rather than developing Garcinol “drugs of last resort” with short-term utility industry has shifted its focus to more lucrative long-term treatments to manage chronic conditions (10 11 A Unique Opportunity for Academia and Computer-Aided Drug Design Given industry’s reluctance to develop novel antibiotics academia is uniquely positioned to play a leading role in the earliest stages of lead identification and optimization (1). In response to this and other opportunities academic drug-discovery centers have already been established at universities in Belgium Sweden the United Kingdom and the United States (12). Success in these new settings depends on adapting industry approaches to the constraints of university research. For example in industry high-throughput screens (HTS) are used to identify pharmacologically active lead antibiotic compounds by testing hundreds of thousands of compounds in highly automated assays (13 14 Unfortunately although robotics and miniaturization have led to increased efficiency traditional HTS is Garcinol beyond the reach of most academic researchers due to its high costs and labor requirements. To make high-throughput testing more tractable many have sought to complement large-scale experimental testing with software that predicts molecular recognition (i.e. ligand binding). Computer-aided drug design (CADD) techniques though still in their infancy have already contributed to the Garcinol discovery and development of a number Garcinol of drugs including captopril dorzolamide boceprevir aliskiren nelfinavir saquinavir zanamivir oseltamivir and raltegravir among others (15). By applying predictive CADD techniques to entire compound databases computational.