Revisiting the β-Lactams for Tuberculosis Therapy with a Compound-Compound Synthetic Lethality Approach

Computational methods, databases and tools for synthetic lethality prediction

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

The Prospect of Repurposing Immunomodulatory Drugs for Adjunctive Chemotherapy against Tuberculosis: A Critical Review

Tuberculosis (TB) remains a global health emergency, with an estimated 2 billion people infected across the world, and 1.4 million people dying to this disease every year. Many aspects of the causative agent, Mycobacterium tuberculosis, make this disease difficult for healthcare and laboratory researchers to fight against, such as unique pathophysiology, latent infection and long and complex treatment regimens, thus causing patient non-compliance with the treatment. Development of new drugs is critical for tackling these problems. Repurposing drugs is a promising strategy for generating an effective drug treatment whilst circumventing many of the challenges of conventional drug development. In this regard, the incorporation of immunomodulatory drugs into the standard regimen to potentiate frontline drugs is found to be highly appealing. Drugs of diverse chemical classes and drug categories are increasingly being evidenced to possess antitubercular activity, both in vitro and in vivo. This article explores and discusses the molecular entities that have shown promise in being repurposed for use in anti-TB adjunctive therapy and aims to provide the most up-to-date picture of their progress.