Prediction Of ADMET Properties Research Articles

Novel derivatives of nitro-substituted salicylic acids: Synthesis, antimicrobial activity and cytotoxicity

Inspired by the high antituberculous activity of novel nitro-substituted derivatives and based on promising predicted ADMET properties we have synthesized a series of 33 salicylanilides containing nitro-group in their salicylic part and evaluated them for their in vitro antimycobacterial, antimicrobial and antifungal activities. The presence of nitro-group in position 4 of the salicylic acid was found to be beneficial and the resulting molecules exhibited minimum inhibitory concentrations (MICs) ranging from 2 to 32μM against Mycobacterium tuberculosis. The best activity was found for 2-hydroxy-4-nitro-N-[4-(trifluoromethyl)phenyl]benzamide (MIC=2μM). 4-Nitrosalicylanilides were also found to be active against all Staphylococcus species tested while for MRSA strain 2-hydroxy-4-nitro-N-[4-(trifluoromethyl)phenyl]benzamide’s MIC was 0.98μM. None of the nitrosalicylanilides was active against Enterococcus sp. J 14365/08 and no considerable activity was found against Gram-negative bacteria or fungi. The hepatotoxicity of all nitrosalicylanilides was found to be in the range of their MICs for HepG2 cells.

Using Cheminformatics in Drug Discovery.

This chapter illustrates how cheminformatics can be applied to designing novel compounds that are active at the primary target and have good predicted ADMET properties. Examples of various cheminformatics techniques are illustrated in the process of designing inhibitors that inhibit both cyclooxygenase isoforms but are more potent toward COX-2. The first step in the process is to create a knowledge database of cyclooxygenase inhibitors in the public domain. This data was analyzed to find activity cliffs - small structural changes that result in drastic changes in potency. Additional cyclooxygenase potency and selectivity trends were obtained using matched molecular pair analysis. QSAR models were then developed to predict cyclooxygenase potency and selectivity. Next, computational algorithms were used to generate novel scaffolds starting from known cyclooxygenase inhibitors. Nine virtual libraries containing 240 compounds each were constructed. Predictions from the cyclooxygenase QSAR models were used to eliminate molecules with undesirable potency or selectivity. Additionally, the compounds were screened in silico for undesirable ADMET properties, e.g., low solubility, permeability, metabolic stability, or high toxicity, using a liability scoring system known as ADMET Risk™. Eight synthetic candidates were identified from this process after incorporating knowledge gained from activity cliff analysis. Four of the compounds were synthesized and tested to measure their COX-1 and COX-2 IC(50) values as well as several ADME properties. The best compound, SLP0020, had a COX-1 IC(50) of 770 nM and COX-2 IC(50) of 130 nM.

Synthesis of N-(3-picolyl)-based 1,3,2λ5-benzoxazaphosphinamides as potential 11β-HSD1 enzyme inhibitors

Inhibition of 11β-HSD1 enzymatic action is perceived as a potential target for the treatment of metabolic syndromes like cardiovascular diseases, obesity, and diabetes. In an attempt to formulate potential organophosphorus compounds against 11β-HSD1 enzyme inhibition, we report novel 6-bromo-3-(6-methyl-2-pyridyl)-2-alkyl/arylamino-3,4-dihydro-2H-1,3,2λ5-benzoxazaphosphinin-2-ones 22a–22n as good in vitro inhibitors of HEK 293 cell lines. The in vivo acute and sub-acute investigations sustain them as good antidiabetic compounds. The computational docking studies performed on them also supported the binding mode of compounds 22a and 22h with 11β-HSD1 protein. The blood glucose level lowering effect of the target compounds 22a–22n screened on the streptozotocin-induced diabetic rats revealed that the target compounds are potential by antidiabetic when compared to the potency of standard glibenclamide drug. In addition, the calculated QSAR parameters, predicted ADMET properties, evaluated bioactivity properties and toxicity risk studies authorize drug like properties to the synthesized compounds.

Synthesis, biological evaluation and molecular modeling study of 2-(1,3,4-thiadiazolyl-thio and 4-methyl-thiazolyl-thio)-quinazolin-4-ones as a new class of DHFR inhibitors

A new series of 2-(1,3,4-thiadiazolyl- or 4-methyl-thiazolyl)thio-6-substituted-quinazolin-4-one analogs was designed, synthesized, and evaluated for their in vitro DHFR inhibition, antimicrobial, and antitumor activities. Compounds 29, 34, and 39 proved to be the most active DHFR inhibitors with IC50 values range of 0.1–0.6μM. Compounds 28, 31 and 33 showed remarkable broad-spectrum antimicrobial activity comparable to the known antibiotic Gentamicin. Compounds 26, 33, 39, 43, 44, 50, 55 and 63 showed broad spectrum antitumor activity with GI values range of 10.1–100%. Molecular modeling study concluded that recognition with key amino acid Glu30, Phe31 and Phe34 is essential for binding. ADMET properties prediction of the active compounds suggested that compounds 29 and 34 could be orally absorbed with diminished toxicity.

Synthesis, molecular properties and DFT studies of new phosphoramidates as potential urease inhibitors

In this work, new phosphoramidates were prepared and screened for their putative urease inhibitory activity. The importance of this class of compounds is related to the wide range of biological activities which they exhibit. Consequently, higher activity shown by phosphoramidates 3a, 4b, 5a, 5b, 5c, and 9a suggests that they could serve as lead substances for the development of novel synthetic compounds with enhanced inhibitory ureolitic activity. Their predicted ADMET properties are also in accordance with the general requirements for drug-like compounds. Structure–activity relationship analyses suggest that the presence of cyclohexylamine group is an important structural feature associated with enhanced activities. DFT calculations were performed to obtain the energy values of HOMO and LUMO, and dipole moment.

Synthesis, biological evaluation, molecular docking and theoretical evaluation of ADMET properties of nepodin and chrysophanol derivatives as potential cyclooxygenase (COX-1, COX-2) inhibitors

Nepodin and chrysophanol, isolated from Rumex nepalensis roots, showed significant cyclooxygenase (COX) inhibitory activity. To further optimize these lead molecules and study structure activity relationship (SAR), eighteen derivatives of nepodin and nine derivatives of chrysophanol were synthesized and evaluated for COX-1 and COX-2 inhibitory potential. Among the synthesized compounds, four nepodin (1f, 1g, 1h and 1i) and three chrysophanol (2e, 2f and 2h) derivatives displayed more pronounced COX-2 inhibition than their respective lead molecule. Further, compounds 1f, 1g, 2e and 2h exhibited better anti-inflammatory activity than ibuprofen in carrageenan-induced rat paw edema assay. Taking into account the in vitro and in vivo results, molecular docking and in silico prediction of ADMET properties of compounds were carried out respectively.

New Developments in Hydrogen Bonding Acidity and Basicity of Small Organic Molecules for the Prediction of Physical and ADMET Properties. Part 2. The Universal Solvation Equation

Theoretical quantifications of hydrogen bonding (HB) basicities and acidities, originally developed for aliphatic systems (J. Chem. Inf. Comput. Sci. 2004, 44, 1042-1055), are now extended to cover aromatic, heterocyclic, anionic, cationic and zwitter-ionic molecular fragments, thus encompassing a majority of druggable chemical space. The addition of terms accounting for cavity formation, polarity, hydrophobicity, and resonance allowed us to derive a new equation able to predict accurately free energies of solvation of diverse solutes, interphase transfers, and aqueous solubilities (log S(w)). We thus provide a "universal solvation equation" (USE) available for the accurate estimation of desolvation energies in protein-ligand docking, for the prediction of many physical and ADMET properties, and for studying fluid phase equilibria.

In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part II: the body in a Hilbertian space

We have broken old surviving dogmas and concepts used in computational chemistry and created an efficient in silico ADME-T pharmacological properties modeling and prediction toolbox for any xenobiotic. With the help of an innovative and pragmatic approach combining various in silico techniques, like molecular modeling, quantum chemistry and in-house developed algorithms, the interactions between drugs and those enzymes, transporters and receptors involved in their biotransformation can be studied. ADME-T pharmacological parameters can then be predicted after in vitro and in vivo validations of in silico models.

In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part I: beyond the reduction of animal model use

There is an urgent need for efficient in silico ADME-T prediction tools for the selection of potent therapeutic drugs as well as the elimination of toxic compounds. This is particularly important in view of the high costs and ethical issues inherent to the use of animal models for drugs filtering. To achieve this mission, not only does the accuracy of in silico tools need to be improved, but also new experts in the field with skills in theoretical chemistry, clinical and fundamental biology have to be trained. Similarly, clinical biologists committed to the obligation of means and legally responsible for the results they generate could establish a legal framework that defines legal responsibilities when performing in silico predictions.

Physician Responses to a Community-Level Trial Promoting Judicious Antibiotic Use

In an environment of multiple campaigns promoting judicious antibiotic use in children, identification of effective strategies is important. We assessed physician responses to a community-level intervention with respect to antibiotic prescribing, related practices, and perceived effectiveness. This study was a mixed qualitative and quantitative evaluation of a randomized controlled community-wide educational intervention in 16 Massachusetts communities. Physicians in intervention communities received locally endorsed guidelines, group educational sessions, and biweekly newsletters. Parents simultaneously received materials in physicians' offices and by mail. After the intervention, we conducted a mailed physician survey and individual interviews to assess the impact of the intervention. We compared survey responses for intervention and control physicians, and we analyzed interview transcripts to provide in-depth information about selected topics. Among survey respondents (n = 168), 91% of intervention and 4% of control physicians reported receiving intervention materials. Physicians received information from multiple other sources. More intervention than control physicians reported decreased antibiotic prescribing from 2000-2003 (75% vs 58%, P = .03), but there were no differences between groups in knowledge, attitudes, or behaviors favoring judicious antibiotic use. Both groups were concerned about antibiotic resistance and reported room to reduce their own prescribing. Interviewed physicians suggested frequent repetition of messages, brief written materials on specific topics for themselves and patients, and promotion in the mass media as the most effective strategies to reduce prescribing. In multiple communities an intervention in physician offices to promote judicious antibiotic prescribing reached its intended audience, but physicians' self-reported attitudes and practices were similar in intervention and control communities. Campaigns that repeat brief, consistent reminders to multiple stakeholder groups may be most effective at assuring judicious antibiotic use.

In silico prediction of ADMET properties: how far have we come?

There have been considerable advances in the last few years in both the quantity and the quality of in silico ADMET property predictions. Most ADMET properties are now computable, and the accuracy of some of the software predictions for physicochemical properties in particular is close to that of measured data. There is, however, universal agreement that more good experimental ADMET data are needed for use in in silico model development, for models are only as good as the data on which they are based. Many data remain confidential but it is to be hoped that, with projects such as the Vitic toxicity database, being developed by Lhasa Limited, pharmaceutical companies will be prepared to release data to an ‘honest broker’ on a confidential basis, so that better in silico models can be developed. Incorporation of calculated ADMET properties into drug discovery and development is a multi-factorial problem and really needs a multi-factorial solution. Some progress is being made in this direction and it is hoped that within the foreseeable future software will be available for this purpose.

ADMET Property Prediction: The State of the Art and Current Challenges

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.

Prediction of ADMET Properties.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.

Prediction of ADMET Properties

This Review describes some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties. The article also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intension of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development.

Computational Pharmaceutical Chemistry – Novel Technologies for Lead Optimization and the Prediction of ADMET Properties

The prediction of affinities of ligands binding to a target protein represents a major challenge in modern computer-aided drug design. To contribute towards this goal, we have developed a new technology to identify feasible binding modes of protein-bound, biomedically interesting molecules and to compute their binding affinity using multidimensional quantitative structure-activity relationships (QSAR). In our approach, the flexibility of the protein is explicitly simulated. Applications of the underlying technology to G protein-coupled receptors, nuclear receptors and cytochrome P450 show the ability of this approach to predict the binding affinity of diverse sets of ligands to a common protein, and suggest its potential to predict adverse or toxic effects of drugs and chemicals in silico.

Predicting ADMET Properties by Projecting onto Chemical Space?Benefits and Pitfalls

The mechanisms behind ADME (absorption, distribution, metabolism, and excretion) related properties and toxicity endpoints are usually complex, and many are not fully understood. As a result, most ADMET predictive models are not based on theoretical principles, but are derived from experimental data. ADMET properties are best analyzed by projecting them onto the compounds of the training set. There are multiple advantages to projecting the ADMET properties from the problem domain to the chemical domain. Projection simplifies the problem, and avoids the entanglement of needing to invoke specific mechanisms. Projection focuses on the most important, and most tractable, aspect of the problem -- the related properties of the compounds themselves. In this review article, the general requirements of the chemical space to be projected are discussed, including the size and diversity of the training set and the accuracy of the biological measurements, and the process is illustrated using an analogue of a real projection. Also, the successes and pitfalls of the projection method in recent ADMET predictions are reviewed. Keywords: admet prediction, qsar, dataset quality, molecular diversity, logp, solubility, blood-brain barrier, absorption, p-glycoprotein, cytochrome p450