Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces

Carbenoid-involved reactions integrated with scaffold-based screening generates a Nav1.7 inhibitor

The discovery of selective Nav1.7 inhibitors is a promising approach for developing anti-nociceptive drugs. In this study, we present a novel oxindole-based readily accessible library (OREAL), which is characterized by readily accessibility, unique chemical space, ideal drug-like properties, and structural diversity. We used a scaffold-based approach to screen the OREAL and discovered compound C4 as a potent Nav1.7 inhibitor. The bioactivity characterization of C4 reveals that it is a selective Nav1.7 inhibitor and effectively reverses Paclitaxel-induced neuropathic pain (PINP) in rodent models. Preliminary toxicology study shows C4 is negative to hERG. The consistent results of molecular docking and molecular simulations further support the reasonability of the in-silico screening and show the insight of the binding mode of C4. Our discovery of C4 paves the way for pushing the Nav1.7-based anti-nociceptive drugs forward to the clinic.

On the hunt for metalloenzyme inhibitors: Investigating the presence of metal-coordinating compounds in screening libraries and chemical spaces

Metalloenzymes play vital roles in various biological processes, requiring the search for inhibitors to develop treatment options for diverse diseases. While compound library screening is a conventional approach, the exploration of virtual chemical spaces housing trillions of compounds has emerged as an alternative strategy. In this study, we investigated the suitability of selected screening libraries and chemical spaces for discovering inhibitors of metalloenzymes featuring common ions (Mg2+, Mn2+, and Zn2+). First, metal-coordinating groups from ligands interacting with ions in the Protein Data Bank were extracted. Subsequently, the prevalence of these groups in two focused screening libraries (Life Chemicals' chelator library, comprising 6,428 compounds, and Otava's chelator fragment library, with 1,784 fragments) as well as two chemical spaces (GalaXi and REAL space, containing billions of virtual products) was investigated. In total, 1,223 metal-coordinating groups were identified, with about a quarter of these groups found within the examined libraries and spaces. Our results indicate that these can serve as valuable starting points for drug discovery targeting metalloenzymes. In addition, this study suggests ways to improve libraries and spaces for better success in finding potential inhibitors for metalloenzymes.

Enhanced Calculation of Property Distributions in Chemical Fragment Spaces

Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.

Relevance of the Trillion-Sized Chemical Space "eXplore" as a Source for Drug Discovery

Within the past two decades, virtual combinatorial compound collections, so-called chemical spaces, became an important molecule source for pharmaceutical research all over the world. The emergence of compound vendor chemical spaces with rapidly growing numbers of molecules raises questions about their application suitability and the quality of the content. Here, we examine the composition of the recently published and, so far, biggest chemical space, "eXplore", which comprises approximately 2.8 trillion virtual product molecules. The utility of eXplore to retrieve interesting chemistry around approved drugs and common Bemis Murcko scaffolds has been assessed with several methods (FTrees, SpaceLight, SpaceMACS). Further, the overlap between several vendor chemical spaces and a physicochemical property distribution analysis has been performed. Despite the straightforward chemical reactions underlying its setup, eXplore is demonstrated to provide relevant and, most importantly, easily accessible molecules for drug discovery campaigns.

Papaverinol-N-Oxide: A Microbial Biotransformation Product of Papaverine with Potential Antidiabetic and Antiobesity Activity Unveiled with In Silico Screening

Bioconversion of biosynthetic heterocyclic compounds has been utilized to produce new semisynthetic pharmaceuticals and study the metabolites of bioactive drugs used systemically. In this investigation, the biotransformation of natural heterocyclic alkaloid papaverine via filamentous fungi was explored. Molecular docking simulations, using protein tyrosine phosphatase 1B (PTP1B), α-glucosidase and pancreatic lipase (PL) as target enzymes, were performed to investigate the antidiabetic potential of papaverine and its metabolites in silico. The metabolites were isolated from biotransformation of papaverine with Cunninghamella elegans NRRL 2310, Rhodotorula rubra NRRL y1592, Penicillium chrysogeneum ATCC 10002 and Cunninghamella blackesleeana NRRL 1369 via reduction, demethylation, N-oxidation, oxidation and hydroxylation reactions. Seven metabolites were isolated: namely, 3,4-dihydropapaverine (metabolite 1), papaveroline (metabolite 2), 7-demethyl papaverine (metabolite 3), 6,4'-didemethyl papaverine (metabolite 4), papaverine-3-ol (metabolite 5), papaverinol (metabolite 6) and papaverinol N-oxide (metabolite 7). The structural elucidation of the metabolites was investigated with 1D and 2D NMR and mass spectroscopy (EI and ESI). The molecular docking studies showed that metabolite 7 exhibited better binding interactions with the target enzymes PTP1B, α-glucosidase and PL than did papaverine. Furthermore, papaverinol-N-oxide (7) also displayed inhibition of α-glucosidase and lipase enzymes comparable to that of their ligands (acarbose and orlistat, respectively), as unveiled with an in silico ADMET profile, molecular docking and molecular dynamics studies. In conclusion, this study provides evidence for enhanced inhibition of PTP1B, α-glucosidase and PL via some papaverine fungal transformation products and, therefore, potentially better antidiabetic and antiobesity effects than those of papaverine and other known therapeutic agents.

Estrogen Receptor-α Targeting: PROTACs, SNIPERs, Peptide-PROTACs, Antibody Conjugated PROTACs and SNIPERs

Targeting selective estrogen subtype receptors through typical medicinal chemistry approaches is based on occupancy-driven pharmacology. In occupancy-driven pharmacology, molecules are developed in order to inhibit the protein of interest (POI), and their popularity is based on their virtue of faster kinetics. However, such approaches have intrinsic flaws, such as pico-to-nanomolar range binding affinity and continuous dosage after a time interval for sustained inhibition of POI. These shortcomings were addressed by event-driven pharmacology-based approaches, which degrade the POI rather than inhibit it. One such example is PROTACs (Proteolysis targeting chimeras), which has become one of the highly successful strategies of event-driven pharmacology (pharmacology that does the degradation of POI and diminishes its functions). The selective targeting of estrogen receptor subtypes is always challenging for chemical biologists and medicinal chemists. Specifically, estrogen receptor α (ER-α) is expressed in nearly 70% of breast cancer and commonly overexpressed in ovarian, prostate, colon, and endometrial cancer. Therefore, conventional hormonal therapies are most prescribed to patients with ER + cancers. However, on prolonged use, resistance commonly developed against these therapies, which led to selective estrogen receptor degrader (SERD) becoming the first-line drug for metastatic ER + breast cancer. The SERD success shows that removing cellular ER-α is a promising approach to overcoming endocrine resistance. Depending on the mechanism of degradation of ER-α, various types of strategies of developed.