From Biological Spectra (multiple protein binding data) to pharmacological profiling!

An ideal drug cures a decease and does not kill a patient (or even lab animals in the course of preclinical testing). Usual drug discovery paradigm is based on studying a compound's properties against a specific, normally decease-related (protein) target. The ability of a compound to bind (inhibit) a specific target is called efficacy.

Even if the efficacy is good, another important property of a compound is its toxicity. Toxicity is related to the compound physical properties, such as solubility etc, as well by its ability to bind to and hence inhibit various vital human proteins (and may be even DNA and RNA).

Common sense suggests that an ideal compound binds its specific drug related target and does not bind to anything else. Anything in between is toxic, at least to a some extent. For example, most of important properties utilize ATP molecules, which means that human body contains a lot of ATP-bindig proteins. If you make a drug attacking an ATP-binding site of a "bad" protein, most probably, a lot of "good" and useful proteins will be also affected. In that case your compound should be toxic. This is indeed the case for many cancer drugs attacking ATP-binding sites of kinases.

The latter statement is the foundation of our approach. Although it's quite conceptually simple, it's useless unless it can be supplemented by a meaningful mathematical model. Let us dwell into some more details to see how the whole thing can be made working.

Let us overview important properties of a drug candidate. First there is a bunch of physical properties, such as solubility, differential solubility, LogP (namely the difference between water and lipid solubility) etc. These quantities are easy to measure, are of direct physical meaning and can be pretty easily calculated (with or without QUANTUM software).

Another set of characteristics defines a compound ability to penetrate through cell membranes and its biochemical in liver. These are quantities deturmining bioavailability, half life, volume of distribution etc. None of such quantities can be evaluated using the simple physical properties alone. For example, drug absorbtion depends on the molecule interaction with proteins actively transporting the molecules through the cell membranes.

The bottom line: bioavailability and other quantities require understanding of a compound binding properties to a selected number of proteins participating in a compound transport and metabolism.

So the conclusion is that IF YOU KNOW WHICH PROTEINS ARE IMPORTANT, AND IF YOU CAN CALCULATE HOW YOUR COMPOUND BINDS TO THEM, YOU KNOW THE COMPOUND PHARMACOLOGICAL AND TOXICOLOGICAL PROPERTIES

Now the only problem how to identify those "important" proteins.

Fortunately, there are thousands of molecules with known properties. What we can do is the following:

- take a molecule
- calculate its binding to any human protein with known 3d structure
- use the obtained binding affinities (numbers) as a molecule's binding profile fingerprint (the Biological Spectrum), characterizing the ability of the molecule to interact with the whole human proteome

Now assume we know such Biological Spectra for 1000s molecules with well known properties. This means we can now datamine the fingerprints->known properites relations. The basic premise is, of course, that the molecules with similar fingerprints have similar properties.

We have a number of proofs of such technology working. The most recent one is the prediction of active transport drug absorption properties for drug like molecues based on the binding data against human brain hexokinase type I-related protein. We prove that the binding energy of a compound against the protein may serve to distinguish between the passively and actively transported molecules and even help to calculated the drug absorbtion quantitatevely.

LD50 vs. MRDD: what's death for a mice is good enough for a man

Prediction of toxic properties of small drug like molecules is a big challenge both from theoretical and practical points of view. Quantitatively people use different measures of toxicity such as Maximum Recommended Daily Dose (MRDD) or Lethal Dose (LD50).

Accurate prediction of such endpoints is only possible if both quantities are "physical" characteristics of a compound, rather than signatures of ever changing views of regulating agencies.

The plot on the left represents the "correlation" between experimental values of MRDD (according to FDA) and LD50 (rat) taken from different sources. As you can see, both quantities have a reasonable degree of correlation for low or intermediate toxicity levels. As soon as toxic compounds are considered, the correlation is lost and apparently no good prediction starting from physical properties of a molecule can be done.

For a moderately toxic molecule we can derive an approximate relation:
-LogMRDD = -LogLD50+2.
In "a human language": the lethal and the maximum recommended dose are roughly two orders of magnitude different; a concentration killing a mice is in fact the maximum recommended for a human being.

q-hERG: QUANTUM's innovative approach to hERG binding calculations is finally released

QUANTUM hERG (q-hERG) screening assays is a unique and innovative computational approach, which allows you to predict from a molecule structures of compounds their inhibition constants (IC50) for hERG channels.

q-hEARG features:

• Output is pIC50 values (-logIC50) for the molecules. The accuracy of prediction is 1.1 pIC50 units;
• No training sets or QSAR methods applied;
• hERG inhibition prediction is made by docking of compound on Quantum Pharmaceuticals’ Proprietary Flexible 3D structure of hERG;
• Docking is based on quantum and molecular physics (see Quantum Science Core for an overview);
• Average correlation has RMSD=1.18 pIC50 unit, and correlation coefficient = 0.82;
• Easy to use user interface, no special hardware requirements, both Linux/Windows supported;
• You can also request services based on QUANTUM hERG Screening Assays.

q-hERG is an independent software module, sharing the user interface and basic usage concepts with our q-ADME: ADME/PK properties prediction software, q-Mol: physico-chemical properties calculator, and q-Tox: toxicological profiling software. More information, including q-hERG product booklet can be obtained from the Quantum Pharmaceuticals products site.

Obtaining Q-Albumin software:

Please review your licensing options, add Q-Albumin: QUANTUM Albumin Binding Prediction Software to your shopping card and checkout to get the download links.

Licensing Options: One Month License $50,00 One Year License $100,00

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Drug likeness: what do bioavailability and toxicity properties tell us about druglikeness?

Druglikeness is a qualitative concept used in drug design for an estimate on how "druglike" a prospective compound is. Usually it is estimated from the molecular structure, often even before the substance is synthesized and tested.

A good drug should show good availability, low toxicity and high potency. The quantitative measures of such properties are bioavailability (BA, measured in %), Maximum Recomended Daily Dose (MRDD, mmol/L) and IC50 against a drug's target.

The product of toxicity and availability, MRDD*BA, gives an upper bound on target IC50 and hence is an indication of a drug quality. The Figure above represents the distribution of such product for slightly over 100 drugs. As it can be seen from the Graph, most of drug compounds have the product small, roughly below 2*10^-5mol/L. Hence, small value of MRDD*BA product may be regarded as an indication of druglikeness.

In fact the situation gets even more interesting if the same druglikeness parameter is plotted in log-scale (see the Figure on the right). Since MRDD*BA limits drug's IC50 against its target, we can deduce that most drugs are centered around pIC50 = 5 (which means that the target pIC50 should exceed 5).