(Q) SAR study on the metabolic stability of steroidal androgens

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

MetStabOn-Online Platform for Metabolic Stability Predictions

Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound's activity is not an easy task due to the extreme complexity of metabolic pathways in living organisms. In this study, the platform for in silico qualitative evaluation of metabolic stability, expressed as half-lifetime and clearance was developed. The platform is based on the application of machine learning methods and separate models for human, rat and mouse data were constructed. The compounds' evaluation is qualitative and two types of experiments can be performed-regression, which is when the compound is assigned to one of the metabolic stability classes (low, medium, high) on the basis of numerical value of the predicted half-lifetime, and classification, in which the molecule is directly assessed as low, medium or high stability. The results show that the models have good predictive power, with accuracy values over 0.7 for all cases, for Sequential Minimal Optimization (SMO), k-nearest neighbor (IBk) and Random Forest algorithms. Additionally, for each of the analyzed compounds, 10 of the most similar structures from the training set (in terms of Tanimoto metric similarity) are identified and made available for download as separate files for more detailed manual inspection. The predictive power of the models was confronted with the external dataset, containing metabolic stability assessment via the GUSAR software, leading to good consistency of results for SMOreg and Naïve Bayes (~0.8 on average). The tool is available online.

An Automated High-Throughput Metabolic Stability Assay Using an Integrated High-Resolution Accurate Mass Method and Automated Data Analysis Software

Advancement of in silico tools would be enabled by the availability of data for metabolic reaction rates and intrinsic clearance (CLint) of a diverse compound structure data set by specific metabolic enzymes. Our goal is to measure CLint for a large set of compounds with each major human cytochrome P450 (P450) isozyme. To achieve our goal, it is of utmost importance to develop an automated, robust, sensitive, high-throughput metabolic stability assay that can efficiently handle a large volume of compound sets. The substrate depletion method [in vitro half-life (t1/2) method] was chosen to determine CLint The assay (384-well format) consisted of three parts: 1) a robotic system for incubation and sample cleanup; 2) two different integrated, ultraperformance liquid chromatography/mass spectrometry (UPLC/MS) platforms to determine the percent remaining of parent compound, and 3) an automated data analysis system. The CYP3A4 assay was evaluated using two long t1/2 compounds, carbamazepine and antipyrine (t1/2 > 30 minutes); one moderate t1/2 compound, ketoconazole (10 < t1/2 < 30 minutes); and two short t1/2 compounds, loperamide and buspirone (t½ < 10 minutes). Interday and intraday precision and accuracy of the assay were within acceptable range (∼12%) for the linear range observed. Using this assay, CYP3A4 CLint and t1/2 values for more than 3000 compounds were measured. This high-throughput, automated, and robust assay allows for rapid metabolic stability screening of large compound sets and enables advanced computational modeling for individual human P450 isozymes.

Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes

BACKGROUND

The most important factor affecting metabolic excretion of compounds from the body is their half-life time. This provides an indication of compound stability of, for example, drug molecules. We report on our efforts to develop QSAR models for metabolic stability of compounds, based on in vitro half-life assay data measured in human liver microsomes.

METHOD

A variety of QSAR models generated using different statistical methods and descriptor sets implemented in both open-source and commercial programs (KNIME, GUSAR and StarDrop) were analyzed. The models obtained were compared using four different external validation sets from public and commercial data sources, including two smaller sets of in vivo half-life data in humans.

CONCLUSION

In many cases, the accuracy of prediction achieved on one external test set did not correspond to the results achieved with another test set. The most predictive models were used for predicting the metabolic stability of compounds from the open NCI database, the results of which are publicly available on the NCI/CADD Group web server ( http://cactus.nci.nih.gov ).

Pharmacokinetics in drug discovery

The aim of this current review is to summarize the present status of pharmacokinetics in Drug Discovery. The review is structured into four sections. The first section is a general overview of what we understand by pharmacokinetics and the different LADMET aspects: Liberation, Absorption, Distribution, Metabolism, Excretion, and Toxicity. The second section highlights the different computational or in silico approaches to estimate/predict one or several aspects of the pharmacokinetic profile of a discovery lead compound. The third section discusses the most commonly used in vitro methodologies. The fourth and last section examines the various approaches employed towards the pharmacokinetic assessment of discovery molecules; including all the LADME processes, discussing the different mathematical methodologies available to establish the PK profile of a test compound; what the main differences are and what should be the criteria for using one or another mathematical approach. The major conclusion of this review is that the use of the appropriate preclinical assays has a key role in the long-term viability of a pharmaceutical company since applying the right tools early in discovery will play a key role in determining the company's ability to discover novel safe and effective therapeutics to patients as quickly as possible.

Methods for Predicting Human Drug Metabolism

Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.

Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.