In silico drug design, sometimes referred to as computational medicine, is the application of in silico research towards problems regarding health and medicine. “In silico” methodologies refer to computer simulations in which researchers can diagnose, treat, and prevent different diseases and ailments.
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By exploiting the exponential advancements that have occurred in the fields of computation and data processing it is now both possible, and practical to simulate real biological pathways through virtual environments. This modus is often geared towards ligand-based medicine and is a vital division of drug modeling and preparation. The bioactive ligands themselves have granted us a plethora of novel data regarding drug and protein targets.
The nature of In Silico Drug Design.
After its first conception in 1989 at the “Cellular Automata Theory and Applications Workshop” at the National Autonomous University of Mexico, it is now the front runner of many fields such as radiation oncology, genetics, physiology, and biochemistry.
In the future, we will see a positive correlation between the exponential growth of technology, and the practicality of in silico drug design, given that this process is grounded in computation and data storage. This was first apparent in 1990, as our aptitude towards processing and data storage increased, allowing for the entirety of the human genome to be sequenced.
Silico drug design and modeling now pose a very attractive alternative to human and animal testing. It has even been hypothesized that these models can render testing on any living organism, completely obsolete. To take it one step further, virtual patients may become conventional, replacing actual patients or cadavers within the medical school curriculum.
Model complex software (e.g. LEGEND, CrystalDock, GANDI) are used to analyze molecular interactions of interest, identifying potential binding fragments for future drugs. These virtual fragments are linked to previously known docking and protein binding sites (receptors). Using a combination of genetic algorithms and chemical/virtual screening, new drugs can be developed at a much faster rate.
How in Silico can be Used within Drug Design
Through In silico medicine, we can now obtain an earlier prediction of success regarding different medicinal compounds, and we can garner a better understanding of the adverse side effects of drugs earlier on in their discovery process.
We saw our first case of vaccine rendering via genomic information way back in 2003, a technique christened as reverse vaccinology. Using genomic information and sequencing performed from microbial genomes, potential antigens can be discovered. The elucidations of each antigen, and each corresponding pathogen, have led to a revitalization in vaccine development.
An example of this can be found in the work of Alessandro Sette’s team of immunologists, developing a vaccine against serogroup B meningococcus. The computational screening of vaccination targets revealed the T cell epitopes CD8+ and CD4+, which led to a clearer understanding of pathogenetic immunity towards vaccination, which corresponds to a better understanding of the nature of T cells as a whole.
The process of Ligand Design Using In Silico Methodologies
These ligands are first prepared and modeled through computational means, acquiring previously derived data to obtain a two-dimensional or three-dimensional structure of the parent molecule, and then relating this data to other ligand interactions. This technique is performed by using prior models of biological systems to predict the molecular dynamics in a medicinal context.
The next step in ligand design is target predication. This process involves predicting drug targets through computational extrapolation tools. Through homology modeling, and the digital assaying of biochemical pathways, the active site and binding site of different drugs can often be found without the use of any wet chemistry or lab workup. By circumventing this process, both medical and research centers have saved both time and resources while on their trek towards drug discovery.
The third and final step in drug discovery, just before trials, is In-Silico bioactivity analysis. This is accomplished through chemical screening, and or virtual screening.
Chemical screening is performed on the lab bench after digital assays narrow down a large number of chemical compounds to be tested for a specified biological function.
In this form of assay, a large sum of compounds is tested for biological targets such as channel proteins, hormone receptors, and others. The biological function of these ligands is often studied within test tubes.
For example, a fixed amount of target protein is kept in test tubes, within an appropriate buffer medium. Once this is done, different compounds selected from a predetermined library are added to the test tubes at a fixed concentration. Then the inhibitory activity of the compound is measured using an appropriate test. Compounds showing inhibitory activity are taken as hits and are selected for the next round of assay to determine their operating inhibitory activity, to find the most potent inhibitor. This is generally done with the help of robotics.
In virtual screening, the binding affinity of compounds from a data library is calculated for an in-silico biological target using molecular docking programs. The veracity of these results is evident and can later be confirmed through experimental procedures.
Within the field of biochemistry, it is often repeated that structure equates to function, and understanding the structure of ligands, protein targets, and other molecular machines through in silico methods can aid in the discovery of drug-resistant mechanisms. It will also evolve precision medicine and can be used to understand the underlying causes of many inherited diseases.
Determining the allostery of these molecules can help us identify alternative drug targeting sites and advance our foundations of drug design as a whole.
Sources:
- In Silico Pharmaceutical Property Prediction. Molecular Pharmaceutics 2007 4 (4), 487-488
- DOI: 10.1021/mp700078w
- Yoshifumi Fukunishi, Satoru Kubota, Haruki Nakamura. Noise Reduction Method for Molecular Interaction Energy: Application to in Silico Drug Screening and in Silico Target Protein Screening. Journal of Chemical Information and Modeling 2006 46 (5), 2071-2084
- DOI: 10.1021/ci060152z
- Sette A, Rappuoli R. Reverse vaccinology: developing vaccines in the era of genomics. Immunity. 2010;33(4):530-541. doi:10.1016/j.immuni.2010.09.017
- Marirosa Mora, Daniele Veggi, Laura Santini, Mariagrazia Pizza, Rino Rappuoli, Reverse vaccinology. Drug Discovery Today, Volume 8, Issue 10, 2003, Pages 459-464, ISSN 1359-6446, doi:10.1016/S1359-6446(03)02689-8.
- Deisboeck TS, Zhang L, Yoon J, Costa J. In silico cancer modeling: is it ready for prime time?. Nat Clin Pract Oncol. 2009;6(1):34-42. doi:10.1038/ncponc1237
Further Reading