Overview

               Tuberculosis, which is a disease caused by the Mycobacterium tuberculosis (MTB) bacteria, remains to be one of the major public health concerns here in the Philippines. In the country, TB is the sixth biggest cause of death; thus, causing it to have one of the highest incidences of TB in Asia. Moreover, owing to the poor prescribing practices of physicians, as well as the sub-optimal compliance of patients with the antibiotic regimen, MTB strains which are resistant to known TB drugs (MDR-TB and XDR-TB) have been continually emerging, making the campaign for successful TB treatment very difficult to achieve. In addition to the problem of the emergence of resistant strains, drugs which are currently in use today for the treatment of TB have been producing undesirable side effects such as liver damage, blurred vision, abdominal pain, gastrointestinal problems, anemia, and bleeding. It is therefore imperative to search for more potent, selective, and safer drugs for the treatment of MTB infections, especially the drug resistant ones.

               The process of drug discovery and development is a relatively time consuming and expensive process, starting from the identification of candidate compounds, to the synthesis, characterization, and the evaluation for therapeutic efficacy. Computational methods, or in silico approaches, have made it possible to accelerate the pace of lead compound identification, validation, and optimization for drug discovery research. One of the most highly useful computational approaches in the search for novel drugs is structure-based virtual screening. This method involves the screening of large collections of databases of chemical structures (more commonly known as compound libraries) and then identifying which of these will bind favorably to, or potentially inhibit, a macromolecular target of interest (enzyme or receptor) that is established to be essential in the pathogenesis of the disease being studied. Docking simulations, in combination with scoring algorithms, can then evaluate the interactions and binding affinities of the ligands to the target, which in turn will yield information that can be utilized in prioritizing which compounds are to be synthesized and assayed for in vitro and in vivo studies.

               The establishment of a computer-aided drug discovery research in UP Manila is the first in the Philippines, where it aims to respond to the pressing TB concerns of the Filipino people. Our research group is particularly interested with discovering new anti-tuberculosis drugs through virtual screening of numerous chemical libraries against novel TB enzyme targets. The computational research part is primarily composed of identification of target enzymes, structure-based pharmacophore screening, docking simulations, and in silico ADMET and TOPKAT calculations. The generated list of high-scoring, high-binding lead compounds that have favorable toxicity and solubility properties will then be prepared through chemical synthesis or through procurement from suppliers. Lastly, these compounds will then be subjected to in vitro cytotoxicity assay in order to further confirm their anti-TB activites.