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Toxicity prediction of a Molecule





 To predict the toxicity of a potential lead molecule.




Molecule is formed when two or more atoms combine together chemically (ex: H2). A compound is said to be a molecule when it contains at least two different elements (ex: H2O). All compounds can be called as molecule but all molecules are not compounds. In terms of pharmacology or in biochemistry, a small molecule is an organic compound which has low molecular weight and may act as a substrate or inhibitor. In medical field, the term is restricted to the molecule that binds to a biopolymer and act as an effector. Most of the small molecules are drug molecules.


Drug molecules are potential lead molecules which act as therapeutic agents and gives beneficiary effects. To come up with single potential lead molecule it takes 12 -16 years. Besides the beneficiary aspects, there may be adverse effects also when using drug/potential lead molecules.


It has been known that, most well known drugs are poisonous substances. All useful drugs produce unwanted effects due to complex nature of human body. Some drugs are more adverse and can produce dangerous effects. So, toxicity is more important measurement during the synthesis of a molecule. One knows that it is difficult to synthesize a potential lead molecule in a shorter time period by undergoing all types of tests.


Computer Aided Drug Designing approaches to design a drug molecule using different tools to predict the pharmacokinetic properties (what the body does to the drug when the drug is administered). The pharmacokinetic properties is also stated as ADME-Tox (Absorption ,Distribution, Metabolism, Distribution and Toxicity).


Drug failures due to toxicity can only be known in the later stages of clinical trials. To minimize the time required by these clinical trials, determination of toxicity potential as early as possible using Insilco prediction is very essential. With the richness of combinatorial library and high throughput screening, prediction on drug toxicity is easier and possible even before the synthesis of the molecule.

(* Insilco – expression used to mean “performed on computer or via computer simulation”)


Synthesizing a single new drug molecule typically takes 12 -16 years and in most of the cases these molecules are rejected because of failure in clinical trials at the level of toxicity. Pharmaceutical companies have recently come up with ADME and toxicity test with the help of insilico based approaches. These approaches can be used to predict the toxicity of a drug molecule even before its synthesis. Even though the insilico approaches are quiet easier, there are problems to overcome.


1. Toxicity may refer to a wide range of effects like carcinogenicity, cytotoxicity etc.
2. There is insufficient data, particularly in the case of humans.
3. The insilico methods are class specific, determining whether toxicity is on or off are least accurate.


Drug molecules can cause toxicity in many ways, like it may not be the drug itself that causes the toxicity, the metabolite may also cause some unwanted effects. In some cases, the cytotoxic and mutagenic properties of the drug molecules are selected to kill the diseased or cancer cells but it has a high probability that it may also affect normal cells.


The simplest form of toxicity is cytotoxicity, where the drug molecule or its metabolite causes serious damage to the cells. Cells of a specific organ, can cause malfunction of kidney, eyes and ears or abnormal clotting of blood, etc. Aspirin is the known example to cause stomach ulcers, mainly if there is a overdose.


Carcinogencity: Some drugs may cause mutation or damage to DNA , which in turn changes cell metabolism and may cause tumors. These drugs may also activate oncogenes leading to transformation of normal cells to cancerous cells. Stilbosterol is one of the good example for carcinogenic agent..


Mutagencity: Some drug molecules cause changes to DNA of germ cells, leading to mutations which offsprings inherit.

Drug Allergy: The allergy caused due to a drug is referred to as Drug Allergy. Antibodies developed within the body for a drug may overreact and form allergies like itching, rashes, etc.


Toxicity measurement


Toxicity is a quantity that can be measured; the simple measure of toxicity is LD50. It is a drug dose which kills 50% of treated animals within a period of time. The therapeutic window gives the range of the dosage between the minimum effective therapeutic concentration and the minimum toxic concentration.


Figure 1: Graph of Therapeutic window


There are many tools to predict the toxicity of a molecule, some of them are commercial, some are online web servers and few of them are freely downloadable.


PreADMET is one of the online servers to predict ADME, toxicity, Drug likeness and molecular descriptor calculation. PreADMET predicts the mutagencity and carcinogencity of compounds, so that toxicity is avoided in compounds.


The input compounds given to PreADMET server is either by drawing the molecule or by uploading the “mol” format of that compound which is to be predicted. The compounds structure or the ”mol” format can be obtained from different chemical databases like drug bank, Chembl, Pubchem, etc.


PreADMET tools uses the strategy to obtain the model which can be used to predict absorption, distribution and toxicity

The strategy steps used are described below.


1. Take a set of compounds, calculate molecular descriptors for all compounds.

2. The set of compounds can be divided into training , validation , and external dataset using principal component analysis and cell based compounds selection.

3. To get most promising dataset, filter the molecular descriptors.

4. Use Genetic Functional Approximation (GFA) to find the best descriptors set for a training set . Information regarding GFA algorithm can be obtained

5. Batch run Rprop neural net using descriptors in op neural net in each equation obtained by GFA learning

6. The model having minimum RMSE is choosen as the best model in both training and validation sets

7. Thus evaluate the performance of trained artificial neural network

8. Validate the final model using external dataset for testing.



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