RepurposeDB - Help

---

Introduction:

Right drug in right dose for right patient at right time via right route is defined as one of the promising outcome of personalized, multi-omic medicine. Systematic drug repositioning enables the translation of insights from computational analyses coupled with laboratory based or experimental findings to bench side in a shorter span of time. From the first report of drug repositioning in early 1950s until 2015, more than 250 successful reports have been published in the literature. We analyzed the repertoire of such cases reported and derived a molecular code for drug repositioning. The analyses of the data in the format of drugs, primary indications and secondary indications reveal molecular preferences; ligand classes, functional roles and pathways were drug repositioning was successful. We envisage our first report on global analyses of successful drug repositioning space and RepurposeDB could help to develop predictive models of drug repositioning and can have major influence in personalized drug repositioning, an emerging therapeutic area in data-driven, individualized medicine.

Drug Repositioning:

Drug repositioning is an important component of therapeutic stratification in the precision medicine paradigm. Molecular profiling and more sophisticated analysis of longitudinal clinical data are refining definitions of human diseases, creating needs and opportunities to re-target or reposition approved drugs for alternative indications. Drug repositioning strategies have demonstrated success in complex diseases requiring improved therapeutic interventions as well as orphan diseases without any known treatments. An increasing collection of available computational and experimental methods that leverage molecular and clinical data enable diverse drug repositioning strategies. Integration of translational bioinformatics resources, statistical methods, chemoinformatics tools and experimental techniques (including medicinal chemistry techniques) can enable the rapid application of drug repositioning on an increasingly broad scale. Efficient tools are now available to systematically apply drug-repositioning approaches to large repositories of compounds with biological activities.

Why drug repositioning?

Developing a new drug from the discovery phase to market requires significant investments in time (~15 years) and resources (USD $800- $1 billion). Chemical screening, lead identification, biological experimentation by in-vitro and in-vivo validation studies and extensive multi-center clinical trials. Lead molecules are also rigorously assayed to define pharmacological effects, bioavailability, the optimal dosing and formulation, and potential toxicity assessments. Irrespective of the dynamics in pharmaceutical, pre-clinical and drug-development budgets, drugs are often burdened with known and unknown side effects that lead to the recall of the drug (See: FDA MedWatch and FDA Adverse Event Reporting System (FAERS) Statistics). These aspects influence the product development and revenue cycles of pharma companies, which in turn affect the pricing of the drug. United States patent rules can also limit the return of the investment (ROI) for pharmaceutical companies due 20- year patent rule with an additional five years of patent exclusivity based on the Hatch-Waxman act (Public Law 98-417). In such a scenario patients, care providers or payers have to share the high cost of medications By contrast, drug-repositioning investigations can bring new therapies to market in approximately half the budget and time required by traditional drug development cycle. This is possible due to the availability of pre-existing data on efficacy, toxicity and dosing along with prior biological knowledge of the compounds. Compared to traditional drug discovery pipelines that screen thousands of molecules with known and unknown toxicity profiles, drug repositioning focuses on experimentally verified, FDA approved molecules or pre-clinical compounds that are either an already successful drug or drugs that are retracted due to adverse reactions when used against the primary indication. This targeted re-use and recycling approach helps in reducing the cost of drug discovery pipelines and thus could help to reduce the cost to the patient population, further enabling the patients to access better therapeutics with shorter times for translation of therapies from clinical research to therapeutic interventions.

Relicensing a compound for new indications may also help the pharmaceutical companies and experimental investigators expand the patent exclusivity by altering the mode of delivery, dosing or via combination therapy. Due to off-label use, repositioning an off-patent drug in the United States might not be a financially lucrative option for pharma companies, it could help inform policy efforts in other countries to consider drug repositioning strategies as part of drug discovery lifecycle. For example repositioning a cheap and widely available drug for malaria or tuberculosis for a new indication with costly therapeutic options can make the treatment affordable to larger, underserved patient communities. Patents, policies and cost estimate around drug repositioning are evolving in different countries. Unified guide- lines to protect drug repositioning based indications and pro- vide affordable therapeutic options based on existing indications in faster turnaround time could ultimately improve therapeutic outcomes.

RepurposeDB: Towards a global catalog of drug repositioning:

More than 300 drug-repositioning examples were reported in the literature and a catalog is developed. A striking observation from the drug repositioning catalog is the importance of different axes of similarity between the disease states and drug groups that are implied by specific drug repositions. For ex- ample intra-disease category repositioning (primary and secondary indications are in the same sub or primary category of ICD-9 coding system), inter-disease category repositioning (primary and secondary indications are in different primary category of ICD-9 coding system), comorbid conditions and underlying pathophysiological modules could also drive successful repositioning. A subset of cancer drugs re- purposed to another class of diseases in the ICD-9 classification is provided in. Analyzing a large number of successful drug repositioning examples would help to design predictive models and gain novel insights to global properties of drug repositioning. We curated, indexed analyzed the repertoire of drugs from repositioning investigations reported in PubMed and FDA databases. The data was compiled as a database RepurposeDB; with 250 diseases and 1100 indications. We also designed Minimum Information About Drug Repositioning Investigations (MIADRI), a guideline to effectively capture and reuse data from drug repositioning investigations to streamline the submission of future investigations to RepurposeDB. The analyses of the data in the format of drugs, primary indications and secondary indications reveal molecular preferences; ligand classes, network properties, functional roles and pathways associated with successful drug repositioning were successful. We envisage this first resource and global analytics of successful drug repositioning space could help to develop predictive models of drug repositioning and can have major influence in personalized drug repositioning, an emerging therapeutic area in data-driven medicine.

Medications in RepurposeDB:

Compounds: Protein Drugs:

Diseases in RepurposeDB: