Molecular optimization of APIs ( DRUG SUBSTANCES)
Introduction:
How is this In Silico differ from mature methods ?
Introduction:
Drug discovery and development is a complex, lengthy
process and failure of a candidate molecule at the
development stage can occur as a result of a combination of
reasons, such as poor pharmacokinetics, lack of efficacy,
and/or toxicity.
Improving the pharmacological profile
of a candidate molecule requires the optimization of
numerous, often competing objectives (ie, biological or
chemical properties), to discover the few improved molecules
that represent the best compromise of the multiple criteria
important for a successful drug.
Traditionally, when a
series of compounds with adequate potency had been
identified and the remaining objectives had to be taken into
account, the pharmaceutical industry strived to optimize one
objective at a time – starting with the binding affinity of a
molecule – as part of a process involving the sequential
optimization of each biological property, each time followed
by screening and a large amount of 'tweaking'. This
approach often results in cycles of trial and error and can be a
waste of resources and time.
Standard chemoinformatics methods for optimization, which
are modeled on the traditional experimental optimization
procedures, ignored the multi-objective nature of the problem
and focused on the optimization of each single biological or
chemical property as they became available during the drug discovery
process.
INTRODUCTION+ The reseach and drug designing, development and its introduction, is indeed in a big mission. The central activities of pharmaceutical research are drug design and delivery.+ Drug design or discovery of molecular structure which fit certain receptor sites in the protein.+The role of computational modeling in drug design is highly developed, dominated by mature metnodology involving techniques such as quantitative structure acitivity relationships (QSAR), ligand Docking and Molecular Dynamics.
+The old approach has been supplemented by efficient new technologies such as use of gene chips, combinational chemistry, robots that screen more compounds in a month, high speed computers that point to likely drug targets, laser microscope that capture individual cells and X ray crystallography which help to design drug that are receptor specific and thus work with maximum efficiency.
How is this In Silico differ from mature methods ?
1. Despite tremendous advancement in technology, it is clear that molecular variation of a limited number of compounds may not deliver the desired drug rather the key lies in many variations of many compounds.
As for example, at initial stage discovery of a “lead” compound having some pharmacological activity may be a failure at the late stage of development due to poor ADME characterstics.
Failure of a molecule to be drug at the later stage of development could be extremely costly and loss of valuable time.
2. The bottleneck preventing this industry from attaining enhanced levels of productivity includes
* The time required for and
* Physical limitations of wet lab experiments.
The In Silico drug design has generated much interest because it can effectively reduce the enormous cost and time required to develop a new drug molecule.
OBJECTIVE
- The objective of the current research is to build models of metabolic systems and simulate and analyze them for the rational design of more efficacious drugs. The focus for the research is many dangerous diseases which is of world's most significant health concerns.
- This script will focus on the drug discovery pipeline and how systems biology can play a role in each of the steps, particularly in the identification and validation of new drug targets. Target validation has traditionally been a laborious process, dependent on animal models and in vivo experiments. Scientist have developed Target on viruses , an in silico target validation tool for many bacterial, viral based diseases.
Lead compound.
There are two types,
- Primary lead.
- Secondary lead.
THE PRIMARY LEAD COMPOUND is one, which has been obtained without using extensive information of properties of any other compound.
* Structural analogues of a lead is prepared by substitution of the functional group while keeping the core chemical structure more or less same.
* These analogues exhibit varying degree of pharmacological acivity.
* The process of analogue generation to maximize the desire pharmacological activity is known as “lead optimization”. Which require the application of quantitative structure activity relationship (QSAR). The random screening of vast natural molecules in the conventional way of obtaining a primary lead is time consuming, costly and requires huge man power.
* Most of the existing drugs have been discovered by this technique.
THE SECONDARY LEAD COMPOUND is substantially different in chemical composition and structure from the primary lead compound and its analogues.
- It may be designed or obtained by utilizing the information related to structural properties of the lead molecule to synthesize the particular molecule having optimum pharmacological properties.
- As for example,
Tolbutamide, an oral hypoglycemic drug is the offshoot from Glyprothiazol which is having blood sugar lowering effect.
Obviously, Tolbutamide is pharmacologically superior to the primary lead Glyprothiazol.
Lead Optimization.
It is the most crucial step in drug design process and involves synthesis of series of analogues of the primary lead and testing their pharmacological and toxicological activity to obtain better next generation lead molecule.
- The underlying principle of lead molecule it that any incremental change in the chemical structure produces either positive or negative increment changes in biological activity.
- A systematic study of such cause and effect relationship is called structure activity relationship (SAR) study.
- The present day “Time to market” for a new therapeutic molecule is considerably shorter than the past. This becomes possible due to application of computer aids at various stages of drug design.
- Obviously for efficient lead optimization computer aids application is fast becoming a necessity.
Application of Artificial Neural Network (ANN).
* ANN is applied to correlation studies of the parameters of molecule description and is a part of QSAR.
* ANN may be defined as an attempt to mimic the way the brain does things in order to harness its versatility and its ability to infer and intuit the incomplete or confusing information without any apparently explicit logical process.
* It is superior to the statistical methods because it does not require to construct a model which is often difficult in pharmaceutical field.
* This method is having advantages when number of variable such as molecules description are many and give more statistically significant result with small number of data sets. ANN fitted curve follows the experimental data more closely compared to statistically fitted curve.
3-Dimensional Quantitative structure Activity Relationship. (3D QSAR)
The ANN coupled with computer aided 3 Dimensional (3D) molecular modeling suited for QSAR.
Software.
A number of computer software are available for various stage of drug design as shown in the below.
Computer software required in various phases of drug Design.
Calculation of molecular description
Structure determination
Macro molecular structure determination.
Data base handling.
Drug receptor interaction. Etc.
* Soft ware names.
1. GOLEM – For statistical valildation of QSAR result.2. PROGOL – More advanced than GOLEN, uses rational drescriptors.3. Beagle - Helps to device new discrimination rules for testing.
* Hybrid software – These are combination of genetic algorithm and ANN .
1. GFA – genetic function approximation.
2. EP – Evolutionary programming. GFA and EP are used for molecular description selection which have strong correlation with biological activity.
* 3D-QSAR software.
1. GOLPE
2. MUSEUM – Both are used for selecting most significant variable in 3D-QSAR studies of lead optimization.00
Conclusion:
The computational model for drug designing makes industry’s R&D faster a bit. This new role for informatics—at the core of a new pharmaceutical R&D process focused on delivering more and better drugs in a shorter time period. Human ingenuity should again prove to be the pharmaceutical industry’s ultimate driver in creating treatment for poorly or previously untreated diseases.
