Alternative variables in drug discovery: promises and challenges

Ligand efficiency indices for effective drug discovery: a unifying vector formulation

INTRODUCTION

The area of ligand efficiency indices (LEIs) in drug discovery has developed significantly since the initial publications nearly 20 years ago. A large number of different LEIs have been defined and applied with certain degrees of success and acceptance in the community. An overall view emphasizing more the common elements than the differences is needed.

AREAS COVERED

In this review, the author accentuates the numerical and algebraic relationships among the different LEIs and proposes the notion of 'ligand efficiency index' (LEI) as a vector variable comprising two interrelated components that provide 'direction' and 'distance' along the drug discovery process. The same concept had been suggested before relating to the graphical representation of the content of Structure-Activity Databases (SAR-Databases).

EXPERT OPINION

The extension of the concept of ligand efficiency from a scalar to a vector will help to unify the different formulations by emphasizing the relationship among the different variables. It should also provide an algebraically robust framework to critically assess the value of LEIs, and to incorporate them routinely in various workflows and protocols. Only cautious and rigorous testing by the community could provide a definitive proof of their possible value as reliable optimization variables in drug discovery.

Antitumor effects of berberine against EGFR, ERK1/2, P38 and AKT in MDA-MB231 and MCF-7 breast cancer cells using molecular modelling and in vitro study

BACKGROUND

Berberine is an alkaloid plant-based DNA intercalator that affects gene regulation, particularly expression of oncogenic and tumor suppressor proteins. The effects of berberine on different signaling proteins remains to be elucidated. The present study aimed to identify the effects of berberine against key oncogenic proteins in breast cancer cells.

METHODS

Molecular docking and molecular dynamics simulations were used for EGFR, p38, ERK1/2, and AKT. The effects of berberine and lapatinib on MAPK and PI3K pathways in MDA-MB231 and MCF-7 cells were evaluated using immunoflorescence assays, and the amounts of phosphorylated kinases were compared to total kinases after treating with different concentrations of berberine.

RESULTS

Simulations showed berberine accurately interacted with EGFR, AKT, P38, and ERK1/2 active sites in silico (scores = -7.57 to -7.92 Kcal/mol) and decreased the levels of active forms of corresponding enzymes in both cell lines; however, berberine binding to p38 showed less stability. Cytotoxicity analysis indicated that MDA-MB231 cells were resistant to berberine compared to MCF-7 cells [72 h IC₅₀ = 50 versus 15 μM, respectively). Also, lapatinib strongly activated AKT but suppressed EGFR in MDA-MB231 cells. The activity of EGFR, AKT, P38, and ERK1/2 were affected by berberine; however, berberine dramatically reduced EGFR and AKT phosphorylation.

CONCLUSION

By way of its multikinase inhibitory effects, berberine might be a useful replacement for lapatinib, an EGFR inhibitor which can cause acquired drug resistance in patients.

Molecular modeling, dynamics simulations, and binding efficiency of berberine derivatives: A new group of RAF inhibitors for cancer treatment

RAF kinases are a family of enzymes in the MAP kinase pathway that contribute to the development of different types of cancer. BRAF is the most important member of RAF kinases. BRAF mutations have been detected in 7% of all cancers and 66% of melanomas; as such, the FDA has approved a few BRAF inhibitor drugs to date. However, BRAF can activate CRAF leading to resistance to BRAF inhibitors. Berberine (BBR) is an alkaloid that is widely distributed in different plant species. Several studies have been carried out on the anti-cancer effects of BBR but direct targets of BBR are unknown. In this study, interactions of BBR derivatives against BRAF and CRAF kinases were modeled and predicted using an in silico-based approach. To analyze and identify the residues important in BRAF docking, we modeled interactions of ATP, the universal substrate of BRAF, and found that Lys483 and Asp594 are the most important residues involved in both ATP and BBR binding [(The average score = -11.5 kcal/mol (ATP); Range of scores = -7.78 to -9.55 kcal/mol (BBR)]. In addition to these polar residues, Trp530 and Phe583 are also applicable to the molecular docking of BRAF. We also observed that Asp593 was excluded from the enzyme cavity, while Phe594 was included inside the cavity, making the enzyme inactive. Finally, three alternatives for BBR were identified with dual RAF inhibition effects [The best scores against BRAF = -11.62 kcal/mol (BBR-7), -10.64 kcal/mol (BBR-9), and -11.01 kcal/mol (BBR-10); the best scores against CRAF = -9.68 kcal/mol (BBR-7), -9.60 kcal/mol (BBR-9), and -9.20 kcal/mol (BBR-10)]. Direct effects of BBR derivatives against BRAF and CRAF kinases had not yet been reported previously, and, thus, for the first time, we report three cycloprotoberberines as lead compounds against RAF kinases.

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

INTRODUCTION

Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.

Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites

BACKGROUND

A recent comparison showed the extensive similarities between the structural properties of metabolites in the reconstructed human metabolic network ("endogenites") and those of successful, marketed drugs ("drugs").

RESULTS

Clustering indicated the related but differential population of chemical space by endogenites and drugs. Differences between the drug-endogenite similarities resulting from various encodings and judged by Tanimoto similarity could be related simply to the fraction of the bitstrings set to 1. By extracting drug/endogenite substructures, we develop a novel family of fingerprints, the Drug Endogenite Substructure (DES) encodings, based on the ranked frequency of the various substructures. These provide a natural assessment of drug-endogenite likeness, and may be used as descriptors with which to derive quantitative structure-activity relationships (QSARs).

CONCLUSIONS

"Drug-endogenite likeness" seems to have utility, and leads to a simple, novel and interpretable substructure-based molecular encoding for cheminformatics.

Lead optimization attrition analysis (LOAA): a novel and general methodology for medicinal chemistry

Herein, we report a novel and general method, lead optimization attrition analysis (LOAA), to benchmark two distinct small-molecule lead series using a relatively unbiased, simple technique and commercially available software. We illustrate this approach with data collected during lead optimization of two independent oncology programs as a case study. Easily generated graphics and attrition curves enabled us to calibrate progress and support go/no go decisions on each program. We believe that this data-driven technique could be used broadly by medicinal chemists and management to guide strategic decisions during drug discovery.

Characterization of selective exosite-binding inhibitors of matrix metalloproteinase 13 that prevent articular cartilage degradation in vitro

Matrix metalloproteinase 13 (MMP-13) has been shown to be the main collagenase responsible for degradation of articular cartilage during osteoarthritis and therefore represents a target for drug development. As a result of high-throughput screening and structure-activity relationship studies, we identified a novel, highly selective class of MMP-13 inhibitors (compounds 1 (Q), 2 (Q1), and 3 (Q2)). Mechanistic characterization revealed a noncompetitive nature of these inhibitors with binding constants in the low micromolar range. Crystallographic analyses revealed two binding modes for compound 2 in the MMP-13 S1' subsite and in an S1/S2* subsite. Type II collagen- and cartilage-protective effects exhibited by compounds 1, 2, and 3 suggested that these compounds might be efficacious in future in vivo studies. Finally, these compounds were also highly selective when tested against a panel of 30 proteases, which, in combination with a good CYP inhibition profile, suggested low off-target toxicity and drug-drug interactions in humans.