Are there differences between launched drugs, clinical candidates, and commercially available compounds?

Novel phosphatidylinositol 4-kinases III beta (PI4KIIIβ) inhibitors discovered by virtual screening using free energy models

Herein, the LASSBio Chemical Library is presented as a valuable source of compounds for screening to identify hits suitable for subsequent hit-to-lead optimization stages. A feature of the LASSBio Chemical Library worth highlighting is the fact that it is a smart library designed by medicinal chemists with pharmacological activity as the main priority. The great majority of the compounds part of this library have shown in vivo activity in animal models, which is an indication that they possess overall favorable bioavailability properties and, hence, adequate pharmacokinetic profiles. This, in turn, is supported by the fact that approximately 85% of the compounds are compliant with Lipinski's rule of five and ca. 95% are compliant with Veber's rules, two important guidelines for oral bioavailability. In this work it is presented a virtual screening methodology combining a pharmacophore-based model and an empirical Gibbs free energy-based model for the ligand-protein interaction to explore the LASSBio Chemical Library as a source of new hits for the inhibition of the phosphatidylinositol 4-kinase IIIβ (PI4KIIIβ) enzyme, which is related to the development of viral infections (including enteroviruses, SARS coronavirus, and hepatitis C virus), cancers and neurological diseases. The approach resulted in the identification of two hits, LASSBio-1799 (7) and LASSBio-1814 (10), which inhibited the target enzyme with IC₅₀ values of 3.66 μM and IC₅₀ and 6.09 μM, respectively. This study also enabled the determination of the structural requirements for interactions with the active site of PI4KIIIβ, demonstrating the importance of both acceptor and donor hydrogen bonding groups for forming interactions with binding site residues Val598 and Lys549.

A systems pharmacology approach to investigate the mechanism of Oryeong-san formula for the treatment of hypertension

ETHNOPHARMACOLOGICAL RELEVANCE

Oryeong-san (ORS) is a traditional formula that has long been used for the treatment of dysfunctions of body fluids and electrolyte homeostasis in Korea, China and Japan. Recent reports have shown that ORS may suppress hypertension by controlling the renin-angiotensin-aldosterone system (RAAS) in the kidney, but its action mechanism has not been well defined.

AIM OF THE STUDY

The aim of this study was to decipher the ORS mechanisms in the treatment of hypertension using a systems pharmacology approach.

MATERIALS AND METHODS

The compounds of ORS were obtained from the TM-MC (database of medicinal materials and chemical compounds in Northeast Asian traditional medicine), and the drug-likeness (DL) and oral bioavailability (OB) of the compounds were evaluated. The potential targets of the compounds were identified using various pharmacology databases. To analyze the mechanisms of the ORS for hypertension, a Compound-Target-Disease (C-T-D) network was established with respect to the genes related to hypertension.

RESULTS

A screening evaluation of the DL and OB of the ORS compounds identified a list of 232 active compounds. The pharmacological activity of the targets was investigated by exploring the interaction network between the compounds and the targets. Analysis of the interactions between the compounds and the hypertension-related targets revealed that 14 ORS compounds regulate the RAAS and vasoconstrictors in the kidney.

CONCLUSIONS

This study used the systems pharmacology approach to decipher the mechanisms of action of ORS for the treatment of hypertension. When hypertension drugs and ORS are used in combination for treatment, possible side effects should be considered because most hypertension drugs are related to the RAAS. The results of this study may provide clues to not only analyze the pharmacological activity of ORS for the treatment of hypertension but other diseases as well.

New 3'-O-aromatic acyl-5-fluoro-2'-deoxyuridine derivatives as potential anticancer agents

New aromatic and aliphatic 3'-O-acyl-5-fluoro-2'-deoxyuridine derivatives were synthesized and evaluated as candidates for prodrugs against various cancer cell lines. As the most promising candidate for antimalignant therapeutics was found a dual-acting acyl derivative 7h, which apparently released not only the known anticancer nucleoside, 5-fluoro-2'-deoxyuridine (FdU), but also an additional active metabolite, acetylsalicylic acid, reinforcing thus therapeutic effect of FdU. Promising therapeutic indices showed also some aromatic dicarboxylic acids derivatives decorated with FdU esters (11 and 12).

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs

We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite. This suggests a 'rule of 0.5' mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This 'rule of 0.5' may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.

A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

Background

The current chemical space of known small molecules is estimated to exceed 10⁶⁰ structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.

Results

The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.

Conclusions

Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.

Quantifying the chemical beauty of drugs

Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.

Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness

Adequate bioavailability is one of the essential properties for an orally administered drug. Lipinski and others have formulated simplified rules in which compounds that satisfy selected physiochemical properties, for example, molecular weight (MW) ≤ 500 or the logarithm of the octanol-water partition coefficient, log P(o/w) < 5, are anticipated to likely have pharmacokinetic properties appropriate for oral administration. However, these schemes do not simultaneously consider the combination of the physiochemical properties, complicating their application in a more automated fashion. To overcome this, we present a novel method to select compounds with a combination of physicochemical properties that maximize bioavailability and druglikeness based on compounds in the World Drug Index database. In the study four properties, MW, log P(o/w), number of hydrogen bond donors, and number of hydrogen acceptors, were combined into a 4-dimensional (4D) histogram, from which a scoring function was defined on the basis of a 4D dependent multivariate Gaussian model. The resulting equation allows for assigning compounds a bioavailability score, termed 4D-BA, such that chemicals with higher 4D-BA scores are more likely to have oral druglike characteristics. The descriptor is validated by applying the function to drugs previously categorized in the Biopharmaceutics Classification System, and examples of application of the descriptor are given in the context of previously published studies targeting heme oxygenase and SHP2 phosphatase. The approach is anticipated to be useful in early lead identification studies in combination with clustering methods to maximize chemical and structural diversity when selecting compounds for biological assays from large database screens. It may also be applied to prioritize synthetically feasible chemical modifications during lead compound optimization.