A Collaborative Research Proposal for Computational Drug Modeling and Computer-aided Drug Design for MDR-TB and XDR-TB

A Collaborative Research Proposal for Computational Drug Modeling and Computer-aided Drug Design for MDR-TB and XDR-TB

Swarnlata^b, K. Balasubramanian^a, K. S. Yadav^b and T. K. Khublani^c

^a Lawrence Berkeley Laboratory, University of California, Berkeley, CA,USA 94720;College of Science, California State University, East Bay, Hayward, CA, USA;

^b NIMS INSTITUTE OF ADVANCE SCIENCES & TECHNOLOGY

NIMS UNIVERSTY

JAIPUR, India

^c Department of TB & Chest, NIMS Medical College & Hospital, Jaipur India

This collaborative research seeks innovative state-of-the-art computational modeling of novel computer aided drug design and discovery for TB. Under this collaborative exchange program, research scholar Swarnlata of NIMS University, Jaipur, India will be invited to California, USA to carry out the critical computational modeling research for this topic. The financial support for Swarnlata will be provided by a grant from Prof. Balasubramanian of CA through US Department of Energy/US National Science Foundation. Swarnlata will also be provided state-of-the-art Lawrence Berkeley Lab’s NERSC (National Energy Research Super Computing facility) and will have access to LBNL’s extensive protein data base (PDB) and toxicity data bases. It is anticipated that the proposed research will take a minimum of 1 year during which time Swarnlata will be a research associate in California, USA while maintaining her research scholar status at NIMS, Jaipur. Upon return from USA, Swarnlata will complete her PhD dissertation at NIMS. The collaborative research carried out under his program is expected to be published in international peer-reviewed scientific journals.

The current conventional treatment for Tuberculosis (TB) uses a combination of antibiotics such as rifampicin (RMP) and drugs that inhibit the cell walls of TB bacteria such as EMB or INH which serve as cell wall inhibitors of Mycobacterium tuberculosis. The two antibiotics most commonly used are rifampicin and isoniazid. Although latent TB treatment requires the use of a single antibiotic, active TB is treated with a combination of several antibiotics. However, the Center for Disease Control and Prevention (CDC), Atlanta, GA, USA has warned of the high risks of the treatment using Rifampicin and Pyrazinamide and has revised recommendations against the use of rifampin plus pyrazinamide for treatment of latent tuberculosis infection, due to high rates of hepatotoxicity associated with the combined use of these drugs [1,2]. Drug resistant tuberculosis such as MDR-TB or XDR-TB is transmitted in the same way as regular TB and pose considerable challenges in third world countries. These types of primary resistance are caused due to a resistant strain of TB. A patient with fully-susceptible TB develops secondary resistance either owing to inadequate treatment or due to not taking the prescribed medicine completely, or because of low quality medication [3]. Drug-resistant TB requires longer and more expensive treatments. Multi-drug resistant TB (MDR-TB) arises as a result of resistance to two first-line TB drugs: rifampicin and isoniazid. Extensively drug-resistant TB (XDR-TB) is defined as resistance to three or more of the six classes of second-line drugs [2]. Although the DOTS method of TB treatment developed by Tuberculosis Research Centre, Chennai, India, is showing promising results, challenges continue to remain in his area as newer strains exhibit resistance to common drugs thus requiring development of efficacious drugs that are particularly effective against MDR-TB and XDR-TB bacteria. The proposal is to develop computer aided drug design and analysis methods to come up with innovative and efficacious therapies, particularly when either the current therapeutic methods are less effective or just are not viable due to other serious side effects such as hepatotoxicity resulting in deaths, for example, in some instances by the combined use of Rifampicin and Pyrazinamide.

The proposed computational modeling involves multiple steps starting with ab initio quantum chemical computations of the currently known drugs used for TB such as Rifampicin, Pyrazinamide, Ethambutol (EMB), INH, SM and so on. These computations require super computing facility of University of California Berkeley, LBNL’s NRESC facility. The computations will start with the construction of three-dimensional structures of the drug molecules such as the ones shown in Fig 1., using the Gauss view software that is already running in CA.

Pyrazinamide Rifampicin Isoniazid (INH)

Streptomycin (SM)

Ethambutol (EMB)

Fig. 1 Some of Commonly Used Drugs for the treatment of TB.

The three-dimensional structures constructed with Gauss view will then be optimized by the state-of-the-art quantum chemistry packages such as Gaussian, NWCHEM and GAMESS geometry optimization software. A variety of quantum chemical electronic parameters that are critical to the drug activity such as electronic charges, electrostatic 3D-topographic charge potentials, HOMO-LUMO gaps, molecular hardness, charge densities, natural bond orbital analysis, 3D-Laplacian charge density profiles, electronic charge density contours, etc. These parameters are guided by previous 3D-Quantum-QSAR drug research studies that Prof Balasubramanian’s group has carried out and provide insight into what features of the drug molecules are critical to its drug activity. These rationale drug design strategies are based on two features for guest-host interactions, namely, regiospecific lock-key mechanisms that are determined by the 3D stereochemistry including chirality and electronic features such as electrophillic and nucleophillic regions which control the binding sites and features critical to the guest-host interactions. The basis for these studies has been confirmed by the mechanistic experimental studies such as the ones on EMB. A recent study has shown that this cell wall inhibitor of mycobacterium TB elicits L-glutamate efflux of Corynebacterium glutamicum [4]. There are several studies that have unveiled the mechanisms of action of these drugs so that our proposed computational studies will be tailored to align with these mechanisms.

The second phase of the proposed computational work would involve docking studies of the drug with the host bacteria pertinent to TB. To this end, Lawrence Berkeley’s lab’s protein data base (PDB) and other TB databases and papers [6] provide with crystal structures of the truncated hemoglobin-N from mycobacterium tuberculosis, for example, shown in Fig.2 as well as other relevant structures and DNA genome sequences. The proposed docking studies will clearly provide insights into the active sites of the protein and drug both from the standpoints of electronic regiospecific features and stereospecific features as inferred from the geometries. Moreover, the genome DNA databases will be accessed for insight into the sequences that are critical to the activity.

The third phase of the work will involve quantitative molecular similarity analysis (QMSA) combined with combinatorial chemistry to generate electronically and structurally similar drug molecules that would have the desired efficacy and also all of the required regiospecific and sterospecific properties. Through a clustering technique followed by QMSA we expect to come up with a small test of chemicals which will hen be fed to Gaussian software for optimization followed by docking studies. We will also be carrying out computational toxicological studies on these compounds to assess the potential toxicity risk profiles of these chemicals. The reduced set of chemicals will then form the basis of in vitro studies before they will go for clinical trails.

Fig 2. Crystal structures of the truncated hemoglobin-N from mycobacterium tuberculosis

References:

[1] http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5231a4.htm

[2] http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5511a2.htm

[3] R. J. O’Brien, Drug-resistant tuberculosis: etiology, management and prevention. Semin Respir Infect. 1994 Jun;9(2):104-12. http://www.ncbi.nlm.nih.gov/pubmed/7973169

[4] E. Radmacher, K. C. Stansen, G. S. Besra, L. J. Alderwick, W. N. Maughan, G. Hollweg, H. Sahm, V. F. Wendisch and L. Eggeling, “Ethambutol, a cell wall inhibitor of Mycobacterium tuberculosis, elicits L-glutamate efflux of Corynebacterium glutamicum”, Microbiology 151 (2005), 1359-1368; http://mic.sgmjournals.org/cgi/content/abstract/151/5/1359

[5] http://www.doe-mbi.ucla.edu/TB/; http://www.webtb.org/Gallery/;

[6] S.T. Cole, et al. "Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence" Nature (1998) 393:537-544; T. P. Stinear et al., Genome Res. 2008. 18: 729-741 http://genome.cshlp.org/content/18/5/729.abstract

http://webhost.nts.jhu.edu/target/; http://www.tbdb.org/

Facilities, Equipment and Other Resources in CA, USA

Prof Balasubramanian’s research group has a dedicated cluster of several IBM RS/6000-260 workstations, PCs, Windows based workstations. Prof Balasubramanian’s group also has access to the LBNL/LLNL national super computer facilities with over 100,000 hours/year of CPU time on Berkeley’s NERSC supercomputing machines and close to million hours in Livermore’s supercomputing facility.

Major Software Packages

ALCHEMY II, 2002 Enhanced and modified by the PI for large-scale ab initio quantum chemical computations (CASSCF, direct CI up to 60 million Configurations, excited states, transition moments, density matrices, natural orbitals, CI-properties, etc.).

GAMESS (CASSCF-geometry optimization, MRCI, Molecular electrostatic potentials, contour plots)-Interfaced with ALCHEY 2002 for large-scale computations of drugs and guest-host interactions.

COLUMBUS (MCSCF, MRCI package with geometry optimization and spin-orbit CI)

Gaussian (CASSCF, MPn, QCI, DFT, geometry optimization, vibrational

frequency computations, solution chemistry models, drug research, electrostatic contours)

NWCHEM (Package of molecular structure, and dynamics codes including SO-DFT, scalar DK relativistic codes)

Artificial intelligence package (Developed by the PI for graph theoretical and QSAR theoretical applications to drugs and toxicity studies)

Quantum dynamics time-independent package (for Pharmacokinetics and metabolomics).

Combinatorial Chemistry codes for the enumeration of structures and isomers of drug molecules.

Quantum QSAR Package (for structure property relations)

Quantum Dock package (for guest host interactions)

QMSA package (for quantitative molecular structure Analysis relations)-Generates structurally similar chemicals compared to a given chemical for CADD with respect to activity parameters.