Discovering smart drugs for the stubborn TB bug

Tuberculosis, commonly known as TB, is a disease caused by the infection of a bacterium named Mycobacterium tuberculosis (MTB). It is an airborne disease that afflicts millions of people each year and the second most deadly infectious disease worldwide, behind only the acquired immuno-deficiency syndrome or AIDS. According to World Health Organization (WHO), the estimated number of new cases of TB in 2011 was almost nine million with 1.4 million deaths.

The standard medical treatment for TB usually consists of a series of drugs taken over a span of six to nine months. The most common drug combination prescribed to TB patients involves rifampicin, isoniazid, pyrazinamide, and ethambutol. Unfortunately, MTB is capable of developing resistance against the common drugs (also known as first-line drugs) in use today. The emergence of drug-resistant strains of MTB has been attributed to poor prescribing practices of physicians, low quality of some drugs, and non-compliance of patients to multi-drug regimens.

The resistance to first-line drugs has been termed multi-drug resistant tuberculosis (MDR-TB).  When MDR-TB arises, the so-called second-line drugs such as amikacin, capreomycin, ciprofloxacin, and ethionamide are implemented. The resistance to these second line anti-TB drugs is called extensively drug-resistant tuberculosis (XDR-TB). In 2011, the estimated number of MDR-TB cases was at 630,000, while new cases of XDR-TB were estimated at 40,000 in 2009. Moreover, the present anti-TB drugs produce side effects, primarily liver damage and other adverse reactions like nausea, vomiting, and anorexia.  Interestingly, the common TB drugs administered today were developed way back in the 1960s. With the occurrence of these side effects and emergence of drug resistance at various levels, there is an obvious urgent need for new and better drugs against TB. 

Traditional drug discovery and development is a time-consuming and expensive process, typically involving trial-and-error identification of candidate compounds from natural sources like plants, microorganisms, and marine life. The identified drug candidates (also known as lead compounds) are then synthesized chemically or biologically in the laboratory, and tested for therapeutic efficacy. Fortunately, the advent of powerful computer hardware and reliable drug discovery software has fast-tracked the entire drug discovery and development process.

The initial step in modern drug discovery is the identification of drug target(s). In the case of the TB pathogen, the targets are biomolecules, which are essential for the growth or survival of the bug. For example, isoniazid and ethambutol inhibit the protein involved in cell wall synthesis while rifampicin acts on the enzyme needed in ribonucleic acid (RNA) synthesis. Without a cell wall and RNA, the mycobacterium would die.  Propitiously, useful drug targets can now be determined with the aid of a computer program that can map out the entire network of proteins whose three-dimensional structures can be generated, viewed, and examined in various ways.

Based on the molecular architecture of the target biomolecule (i.e. usually a protein), one can make a computer-generated model of the important structural features (called a pharmacophore) of a compound that makes a good drug. The pharmacophore is the basis of searching for existing compounds available in chemical libraries or databases that could potentially act on the druggable target. This high throughput in silico (i.e. computer-based) procedure is called virtual screening, which is commonplace in the modern lead identification phase of drug discovery. The discovery of a handful of leads from millions of compounds by means of virtual screening significantly abbreviates the drug development pipeline and drastically reduces the cost and time that would have been otherwise spent for random laboratory preparation and testing of chemical entities.  Usually, the lead compounds do not possess optimum drug properties, thus further structural tweaking is necessary. Thank goodness, the final lead optimization phase to improve the ADMET (absorption distribution, metabolism, excretion, toxicity) properties of a molecule can now also be predicted using cutting-edge computational tools. 

Our research group in the University of the Philippines Manila, with copious support from the UP System, is presently utilizing the full range of useful computer programs in drug discovery to discover and develop a new class of designer anti-tuberculosis drugs. Our trailblazing approach in drug discovery is the first of its kind in the Philippines. And we are jubilant that the University of the Philippines dares to lead the country in revolutionizing its drug discovery paradigm.

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Dr. Junie B. Billones is an associate professor of the Department of Physical Sciences and Mathematics, College of Arts and Sciences and a Research Faculty of the Institute of Pharmaceutical Sciences, National Institutes of Health, University of the Philippines Manila. He is the leader of the research program on Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines. He is a member of the Kapisanang Kimika ng Pilipinas (KKP) and American Chemical Society (ACS). He can be reached at [email protected].