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Description

Abstract: Currently, drug discovery approaches focus on the design of therapies that alleviate an index symptom by re-engineering the underlying biological mechanism in agonistic or antagonistic fashion. For example, medicines are routinely developed to target an essential gene that drives the disease mechanism. Therapeutic overloading where patients get multiple medications to reduce the primary and secondary side effect burden is standard practice. This single-symptom based approach may not be scalable, as we understand that diseases are more connected than random and molecular interactions drive disease comorbidities. In this work, we present a proof-of-concept drug discovery strategy by combining network biology, disease comorbidity estimates, and computational drug repositioning, by targeting the risk factors and comorbidities of peripheral artery disease, a vascular disease associated with high morbidity and mortality. Individualized risk estimation and recommending disease sequelae based therapies may help to lower the mortality and morbidity of PAD.

Learning Objective 1: Discuss limitations of current drug discovery and repositioning strategies that lead to therapeutic burden, low efficacy, and improved side effects

Learning Objective 2 (Optional): Explain the need for a new drug discovery and repositioning approach based on disease comorbidities

Learning Objective 3 (Optional): Discuss results of the new method using peripheral artery disease and its risk factors and comorbidities as example

Authors:

Shameer Khader (Presenter)
Icahn School of Medicine at Mount Sinai

Garrett Dow, Mayo Clinic
Benjamin Glicksberg, Icahn School of Medicine at Mount Sinai
Kipp Johnson, Icahn School of Medicine at Mount Sinai
Yi Ze, Mayo Clinic
Max Tomlinson, Icahn School of Medicine at Mount Sinai
Ben Readhead, Icahn School of Medicine at Mount Sinai
Joel Dudley, Icahn School of Medicine at Mount Sinai
Iftikhar Kullo, Mayo Clinic

Presentation Materials:

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