The to apply on large scale to evaluate

The development of a typical prescription drug
costs up to $2.6 billion, and takes longer than a decade to get to market65. When a drug gets approved for use in clinical
practice, its effectiveness differs from patient to patient, sometimes having
no effect at all, not to mention adverse or side effects. More effective ways
are needed to develop new drugs as well as to improve management of existing
drugs for specific patients. To accelerate drug development, scientific
advances and associated technologies (genomics, proteomics, metabolomics, and
bioinformatics) have been incorporated into the process. To improve therapeutic
efficacy, ‘targeted therapy’ has been developed, in which drugs are directed
against disease-specific molecules. Society is in need of many more personalized
drugs, a precision medicine, produced far more rapidly and cheaply than is
currently the case.

 

For the drugs which do have effects, the emergence
of resistance is usually unavoidable, which presents health care systems with
serious challenges. Given the complexity of emergent mutations in
disease-related proteins, the central mechanism for drug resistance,
researchers must invoke computational tools to help understand the mechanisms
of protein function and inhibition, how mutations affect them and how drugs can
be designed to circumvent resistance.

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One of the central quantities of interest when
assessing a potential drug is the thermodynamics approach (evaluating binding
affinity or binding free energy). It determines the strength of the
interaction, which is key for successful pharmaceutical drug development and
the efficacy of clinical intervention. The use of computationally based
molecular modeling and dynamics methodology to calculate the strength of
macromolecular binding free energies is naturally of major interest in drug
discovery and development. Furthermore, computational modeling and simulations
are easier and much cheaper to apply on large scale to evaluate many potential
medicines along with accounting the diversity in any patient genome
differences.

 

Historically, the industry has avoided engagement
with high performance computing due to its lack of accuracy and
reproducibility, and/or long turnaround time. Most existing protocols for
computing free energy changes are regarded as unreliable as well as
computationally expensive. A recent survey by Nature revealed that more than
70% of researchers failed to reproduce another scientist’s results, while more
than half were unable to reproduce their own66. This holds irrespective of the field of research
and applies to both experimental and computational methods. In the case of
experiments, a variety of reasons ranging from mixed up chemicals, through
fluctuations in the environment, variations in the experimental setup to
confirmational bias can be held responsible for non-reproducible results. In
the case of molecular simulations, the reasons reside in a combination of
theory and the model including the accuracy of force fields, convergence of the
calculations, efficiency of software and so on. However, for all traditional
molecular dynamics (MD) based methods, lack of reproducibility is intrinsic and
is independent of these other issues67. This is because the prediction of Gibbs free
energy macroscopic properties requires ensemble averaging over microscopic
states.

 

Recently however, pharmaceutical companies are
becoming more interested in calculating binding free energies due to advances
in technical and practical implementation. In particular, the FEP+
implementation of Free Energy Perturbation (FEP) has shown potential to improve
the ability to predict protein-ligand binding affinities on an industrially
relevant time scale68. Research is ongoing to understand how broadly applicable the method
is, and how accurate its predictions are when applied to active drug discovery
programs. There still exists a gap between published success cases and the
scale of implementation and robustness needed for industrial drug discovery;
such work needs to be performed in a collaborative, non-competitive framework.
Our work in this area, in developing and applying the molecular approach using
GROMACS58, FOLD-X59-60, and Friend with StSNP server61-62 integrated with python automated tool for efficient binding
energy calculation is simple yet rationally significant to make free energy
calculations rapid, accurate, precise and reproducible.

 

Our proposed study will evaluate the potential of
our approach for the improved predictive power for accurate ranking of ligands
by their binding free energies. We will apply the integrated approach on a few
key drug targets in the association study of diabetes and breast cancer for
accurate, precise and reliable free energy predictions. With our method,
comprising of GROMACS58, FOLD-X59-60, and Friend with StSNP server61-62 integrated with the python automated tool, to execute the
workflows of binding affinity calculation will help us complete the whole
execution in less time, depending on the architecture and hardware available.
The accuracy and speed of the calculation will make it possible to perform
actionable decisions in clinical or industrial settings. As large and secure
computing resources become more routinely available in Qatar, for example
through cloud computing, it will become increasingly easy for research groups in
Qatar to access approaches like the one used in this study. Consequently, the
robust prediction of protein?ligand binding affinities in an industrial setting
should become more routine and offer a long awaited development in the field of
structure-based drug design and sequence-based personalized medicine.