The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching, or simple in silico ligand docking. We now describe a novel rapid computational proteochemometric method called "train, match, fit, streamline" (TMFS) to map new drug−target interaction space and predict new uses. The TMFS method combines shape, topology, and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug−target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3671 FDA approved drugs across 2335 human protein crystal structures. The TMFS method predicts drug−target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84%, and 91% for agents ranked in the top 10, 20, 30, and 40, respectively, out of all 3671 drugs. Drugs ranked in the top 1−40 that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an antiparasitic with recently discovered and unexpected anticancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity and angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27 000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets.

Purpose To understand the changes in the microbiome in psoriatic disease, we conducted a systematic review of studies comparing the skin and gut microbiota in psoriatic individuals and healthy controls. Findings Our review of studies pertaining to the cutaneous microbiome showed a trend towards an increased relative abundance of Streptococcus and a decreased level of Propionibacterium in psoriasis patients compared to controls. In the gut microbiome, the ratio of Firmicutes and Bacteroidetes was perturbed in psoriatic individuals compared to healthy controls. Actinobacteria was also relatively underrepresented in psoriasis patients relative to healthy individuals. Summary Although the field of the psoriatic microbiome is relatively new, these first studies reveal interesting differences in microbiome composition that may be associated with the development of psoriatic comorbidities and serve as novel therapeutic targets.

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drugtarget and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Increased R & D spending and high failure rates exist in drug development, due in part to inadequate prediction of drug metabolism and its consequences in the human body. Hence, there is a need for computational methods to supplement and complement current biological assessment strategies. In this review, we provide an overview of drug metabolism in pharmacology, and discuss the current in vitro and in vivo strategies for assessing drug metabolism in preclinical drug development. We highlight computational tools available to the scientific community for the in silico prediction of drug metabolism, and examine how these tools have been implemented to produce drug-target signatures relevant to metabolic routes. Computational workflows that assess drug metabolism and its toxicological and pharmacokinetic effects, such as by applying the adverse outcome pathway framework for risk assessment, may improve the efficiency and speed of preclinical drug development.

Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the “-omics”, wherein an individual’s genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into “big data” informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. “Big data” informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient “-omics” data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of “big data” informatics for clinical pharmacology.