Drug Discovery Through The Ages…
Ever since the start of humanity, humans as a species have tried to alleviate ailments and disorders. Until the early 1800s, traditional medicine was used with uncertain or mixed results. Now we still use these compounds from nature but in a much more processed and potent way than our ancestors.
Initial drug discovery was based on the active ingredients from traditional remedies of old or by opportune discoveries. Since the past three centuries, it has included defined laboratory steps like identifying a target molecule or site, creating assays to find the lead compound for said target and optimizing it (lead compound) to increase its efficacy and affinity and to decrease the potential side effects. As the process has become more complicated, the time, effort and cost of it have also gone up. Along with all this, the type of researchers immersed in this process has also increased from physiologists, chemists and statisticians to incorporate biochemists, molecular biologists, toxicologists and pharmacologists. With the advent of computers and increase in the power of these machines, computer scientists also became more and more involved in assessing, analyzing and matching data from huge data libraries specific to chemicals or gene sequences or even proteins.
The target is a macromolecule (protein or enzyme) that performs a biological function. It is usually called a receptor. This receptor is chosen for its implications in the initiation, continuation or causation of the pursued disease. Target identification begins with the identification of promising therapeutic targets (protein or enzyme). The developed drug will attack or modify the intended target and stop or inhibit that particular pathway for the disease. Earlier, these target ideas used to be found in academic research and scientific literature. This process used to be long and tiring and more often than not, would fail.
In the case of a successful find, the target had to be validated. This required the creation of experimental models and assays to screen and assess the pharmacological association to the pursued disease. Models were also created to test is the modification of the target by the drug would be safe and only inhibit the disease pathway and not any crucial process in the functioning of the body. This process again takes months or years to complete and even then, there’s no guarantee of success.
Then comes the creation of the lead compound. The lead compound is the new chemical developed with specific biologic and pharmacologic properties with the potential to be used as a therapeutic for the targeted disease. This will be tested by assays that have been created for this very purpose. Multiple methods and technologies are used for these tests and they can go on for a year or two, minimum.
These tests need to be reiterated multiple times to optimize the properties of the lead compound. This also allows researchers to record details of the drug-target interaction along with the non-target interaction to make sure that the drug doesn’t end up targeting the wrong protein. The compound that most actively binds to the specific target will become the lead compound. This is also a long process and usually results in failure or a very long, time-consuming journey of tests and negative results as the toxicity and stability of the compound are also tested vigorously.
However, in the past few decades, we have seen a sudden increase in the development and usage of computational methods (in silico). These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modelling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico along with in vitro is frequently used to discover and optimize novel molecules that have an affinity to the target site, have clarification for the ADME and toxicological properties along with physicochemical characteristics.
The term ‘in silico’ is a modern word usually used to mean experimentation performed by a computer. It is related to the biological in vivo and in vitro. The history of this term is not very clear, as numerous scientists claim to be involved in its origin. The first usage of in silico methods can be traced back to as early as the 1960s when the quantitative relationships amongst chemical structure and PD and PK effects in the body were uncovered by computational means. We also know that some of it’s earliest mentions in writing are in the publications by Sieburg (1990) and Danchin (1991).
In silico pharmacology (computer therapeutics) is a swiftly extending subject matter that presides over the development of techniques that use software to capture, analyse and integrate medical and biological data from varied sources across the globe. This technology also defines the aid this information provides in the creation of computational simulations or models that will be used for predicting, hypothesizing and finally providing stepping stones in the medical and pharmacological fields.
In this day and age, in silico methods are a fundamental aspect of drug discovery and development, mainly because they impact the entirety of the drug development pipeline; isolating and determining new potential drug targets with a substantial decrease in the time taken and the cost incurred. Moreover, computer-aided drug design (CADD) is vital for firstly — cutting down the experimental exploitation of animals used for in vivo testing; secondly — promoting designing of more secure drugs; thirdly — repositioning identified drugs and lastly — facilitating drug design, discovery, development and HIT-optimization.
In silico methods cut down the time taken in the drug discovery process by traditional methods by years! It also increases the chances of success by 25%. Traditional methods are more time consuming and have a minuscule success rate of 1–2%. But the same process when undergone using in silico tools takes the success rate to 30%. This is due to a large amount of data access as well as the ability of the computational algorithms to parse through this data and find the information in a matter of hours or days that would take humans months of non-stop research to find. Human error is also avoided by using these algorithms.
In silico methods have increased efficiency, accuracy and chances of success. According to how much data is available, and how much more is being added every day, in silico methods are the key drivers in drug discovery.