Evaluating the performance of drug-repurposing technologies.

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing.

We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing by including ligand-based, data fusion, and decision tree pipelines. The goal of shotgun drug repurposing is to screen and rank every existing human use drug or compound for every disease/indication. The first version of CANDO implemented a structure-based pipeline that modeled interactions between compounds and proteins on a large scale, generating compound-proteome interaction signatures used to infer the similarity of drug behavior; the new pipelines accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We obtain improved benchmarking performance with the new pipelines across all three evaluation metrics used: average indication accuracy, pairwise accuracy, and coverage. The best performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, thereby enhancing our ability to generate putative therapeutic repurposing candidates, and increasing drug discovery efficiency.

Exploration of interaction scoring criteria in the CANDO platform.

OBJECTIVE: Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs. RESULTS: We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.

A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment.

Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

The Problems of Realism-Based Ontology Design: a Case Study in Creating Definitions for an Application Ontology for Diabetes Camps.

A requirement of realism-based ontology design is that classes denote exclusively entities that exist objectively in reality and that their definitions adhere to strict criteria to ensure that the classes are re-usable in other ontologies while preserving their ontological commitment. Building realism-based ontologies is therefore quite challenging and time-consuming, demanding considerable training. Although the top-level in the form of the Basic Formal Ontology (BFO) is worked out very well, and also the upper levels of certain domains, there is still a disconnect with the bottom- up or middle-out approach which is typical, and more practical, for application ontologies. Using the development of an application ontology for diabetes management in diabetes camps as an example, we present an overview of problems trainees in realism-based ontology design can be confronted with and offer some guidelines on how to deal with them in case no ideal solution is available.

Simultaneous orthotopic liver and kidney transplant with repair of abdominal aortic aneurysm: operative timing.

In the case of coexisting abdominal aortic aneurysm (AAA) and liver/renal failure, the controversial issue is the timing of the AAA repair and the transplantation of the affected organs. The question is whether to repair the AAA first and perform the double transplantation at a later time, or to perform all three procedures in the same operative session. This patient was affected by hepatic/renal failure and had also developed AAA. We describe the operative strategies utilized to perform the cadaver donor and recipient operations in this setting. In our patient, a combined liver/kidney transplantation with simultaneous aneurysm repair using arterial allografts was successfully performed. In a patient affected by end-stage liver, kidney, and aneurysmatic disease, a simultaneous liver/kidney transplant and AAA repair may represent the safest and most efficient treatment solution.