Drug-like Properties and Fraction Lipophilicity Index as a combined metric

Design and investigation of novel iridoid-based peptide conjugates for targeting EGFR and its mutants L858R and T790M/L858R/C797S: an in silico study

In this work, we designed novel peptide conjugates with plant-based iridoid and lichen-derived depside derivatives to target the wild-type EGFR (WT) and its mutants, L858R and T790M/L858R/C797S triple mutant. These mutations are often expressed in multiple cancers, particularly lung cancer. Specifically, the iridoids included 7-deoxyloganetic acid (7-DGA) and loganic acid (LG), while the depside derivative was sekikaic acid (SK). These compounds are known for their innate anticancer properties and were conjugated with two separate peptide sequences KLPGWSG (K) and YSIPKSS (Y). These sequences have been shown to target EGFR in previous phage display library screening, although the mechanism is unknown. Thus, we created the di-conjugates for dual targeting and investigated their interactions of the di-conjugates and that of the neat peptides with the kinase domain of EGFR (WT) and the two mutants using molecular docking, molecular dynamics (MD) simulations, and MM-GBSA analysis. Docking studies revealed that the (7-DGA)2-K showed the highest binding affinity at - 9.3 kcal/mol with the L858R mutant, while (LG)2-Y displayed the highest binding affinity at - 9.0 kcal/mol for the triple mutant receptor. Our results indicated that several of the conjugates interacted with crucial residues of the kinase domain, including ASP855 and THR854 (activation loop), MET793 and PRO794 (hinge region), ARG841 (catalytic loop), and LYS728 and LEU718 of the glycine-rich P-loop. Interestingly, strong hydrophobic interactions were also observed with the C-terminal tail residues, such as PHE997 and ALA1000 as well as with ARG999 for the YSIPKSS peptide and most of the conjugates. The hydroxyl group of the cyclopentane ring and the oxygen of the pyran ring of the (7-DGA)2-peptide conjugates contributed to binding particularly in the hinge region, while the peptide components formed an extended structure that bound well into the C-lobe. The (SK)2-Y di-conjugate and KLPGWSG peptide formed hydrogen bonds with the SER797 residue of the triple mutant. Overall, our results show that the (7-DGA)2-K, di-conjugate, the (7-DGA)2-Y di-conjugate, and the neat YSIPKSS demonstrated strong and stable binding with the L858R mutant and the highly resistant triple mutant EGFR, respectively. The novel designed conjugates demonstrate potential for further optimization for laboratory studies aimed at developing new therapeutics for targeting specific EGFR mutant expressing cells.

Antihypertensive and Antidiabetic Drug Candidates from Milkfish (Chanos chanos)-Identification and Characterization through an Integrated Bioinformatic Approach

Integrated bioinformatics tools have created more efficient and robust methods to overcome in vitro challenges and have been widely utilized for the investigation of food proteins and the generation of peptide sequences. This study aimed to analyze the physicochemical properties and bioactivities of novel peptides derived from hydrolyzed milkfish (Chanos chanos) protein sequences and to discover their potential angiotensin-converting enzyme (ACE)- and dipeptidyl peptidase-4 (DPPIV)-inhibitory activities using machine learning-based tools, including BIOPEP-UWM, PeptideRanker, and the molecular docking software HADDOCK 2.4. Nine and three peptides were predicted to have ACE- and DPPIV-inhibitory activities, respectively. The DPPIV-inhibitory peptides were predicted to inhibit the compound with no known specific mode. Meanwhile, two tetrapeptides (MVWH and PPPS) were predicted to possess a competitive mode of ACE inhibition by directly binding to the tetra-coordinated Zn ion. Among all nine discovered ACE-inhibitory peptides, only the PPPS peptide satisfied the drug-likeness analysis requirements with no violations of the Lipinski rule of five and should be further investigated in vitro.

Towards development of new antimalarial compounds through in silico and in vitro assays

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC50 between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC50 = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

Developing a Tanshinone IIA Memetic by Targeting MIOS to Regulate mTORC1 and Autophagy in Glioblastoma

Tanshinone IIA (T2A) is a bioactive compound that provides promise in the treatment of glioblastoma multiforme (GBM), with a range of molecular mechanisms including the inhibition of the mechanistic target of rapamycin complex 1 (mTORC1) and the induction of autophagy. Recently, T2A has been demonstrated to function through sestrin 2 (SESN) to inhibit mTORC1 activity, but its possible impact on autophagy through this pathway has not been investigated. Here, the model system Dictyostelium discoideum and GBM cell lines were employed to investigate the cellular role of T2A in regulating SESN to inhibit mTORC1 and activate autophagy through a GATOR2 component MIOS. In D. discoideum, T2A treatment induced autophagy and inhibited mTORC1 activity, with both effects lost upon the ablation of SESN (sesn-) or MIOS (mios-). We further investigated the targeting of MIOS to reproduce this effect of T2A, where computational analysis identified 25 novel compounds predicted to strongly bind the human MIOS protein, with one compound (MIOS inhibitor 3; Mi3) reducing cell proliferation in two GBM cells. Furthermore, Mi3 specificity was demonstrated through the loss of potency in the D. discoideum mios- cells regarding cell proliferation and the induction of autophagy. In GBM cells, Mi3 treatment also reduced mTORC1 activity and induced autophagy. Thus, a potential T2A mimetic showing the inhibition of mTORC1 and induction of autophagy in GBM cells was identified.

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics

Intracellular tau fibrils are sources of neurotoxicity and oxidative stress in Alzheimer's. Current drug discovery efforts have focused on molecules with tau fibril disaggregation and antioxidation functions. However, recent studies suggest that membrane-bound tau-containing oligomers (mTCOs), smaller and less ordered than tau fibrils, are neurotoxic in the early stage of Alzheimer's. Whether tau fibril-targeting molecules are effective against mTCOs is unknown. The binding of epigallocatechin-3-gallate (EGCG), CNS-11, and BHT-CNS-11 to in silico mTCOs and experimental tau fibrils was investigated using machine learning-enhanced docking and molecular dynamics simulations. EGCG and CNS-11 have tau fibril disaggregation functions, while the proposed BHT-CNS-11 has potential tau fibril disaggregation and antioxidation functions like EGCG. Our results suggest that the three molecules studied may also bind to mTCOs. The predicted binding probability of EGCG to mTCOs increases with the protein aggregate size. In contrast, the predicted probability of CNS-11 and BHT-CNS-11 binding to the dimeric mTCOs is higher than binding to the tetrameric mTCOs for the homo tau but not for the hetero tau-amylin oligomers. Our results also support the idea that anionic lipids may promote the binding of molecules to mTCOs. We conclude that tau fibril-disaggregating and antioxidating molecules may bind to mTCOs, and that mTCOs may also be useful targets for Alzheimer's drug design.

Analysing the effect caused by increasing the molecular volume in M1-AChR receptor agonists and antagonists: a structural and computational study

M1 muscarinic acetylcholine receptor (M1-AChR), a member of the G protein-coupled receptors (GPCR) family, plays a crucial role in learning and memory, making it an important drug target for Alzheimer's disease (AD) and schizophrenia. M1-AChR activation and deactivation have shown modifying effects in AD and PD preclinical models, respectively. However, understanding the pharmacology associated with M1-AChR activation or deactivation is complex, because of the low selectivity among muscarinic subtypes, hampering their therapeutic applications. In this regard, we constructed two quantitative structure-activity relationship (QSAR) models, one for M1-AChR agonists (total and partial), and the other for the antagonists. The binding mode of 59 structurally different compounds, including agonists and antagonists with experimental binding affinity values (pKi), were analyzed employing computational molecular docking over different structures of M1-AChR. Furthermore, we considered the interaction energy (Einter), the number of rotatable bonds (NRB), and lipophilicity (ilogP) for the construction of the QSAR model for agonists (R2 = 89.64, QLMO2 = 78, and Qext2 = 79.1). For the QSAR model of antagonists (R2 = 88.44, QLMO2 = 82, and Qext2 = 78.1) we considered the Einter, the fraction of sp3 carbons fCsp3, and lipophilicity (MlogP). Our results suggest that the ligand volume is a determinant to establish its biological activity (agonist or antagonist), causing changes in binding energy, and determining the affinity for M1-AChR.

Sulfonamide Derivatives: Recent Compounds with Potent Anti-alzheimer's Disease Activity

Facile synthetic procedures and broad spectrum of biological activities are special attributes of sulfonamides. Sulfonamide derivatives have demonstrated potential as a class of compounds for the treatment of Alzheimer's disease (AD). Recent sulfonamide derivatives have been reported as prospective anti-AD agents, with a focus on analogues that significantly inhibit the function of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes and exhibit remarkable antioxidant and anti-inflammatory properties, all of which are critical for the treatment of AD. Sulfonamide- mediated activation of nuclear factor erythroid 2-related factor 2 (NRF2), a key regulator of the endogenous antioxidant response, has also been suggested as a potential therapeutic approach in AD. Additionally, it has been discovered that a number of sulfonamide derivatives show selectivity for the β- and γ-secretase enzymes and a significant reduction of amyloid B (Aβ) aggregation, which have been implicated in AD. The comparative molecular docking of benzenesulfonamide and donepezil, an AD reference drug showed comparable anti-AD activities. These suggest that sulfonamide derivatives may represent a new class of drugs for the treatment of AD. Thus, the current review will focus on recent studies on the chemical synthesis and evaluation of the anti-AD properties, molecular docking, pharmacological profile, and structure-activity relationship (SAR) of sulfonamide derivatives, as well as their potential anti-AD mechanisms of action. This paper offers a thorough assessment of the state of the art in this field of study and emphasizes the potential of sulfonamide derivatives synthesized during the 2012-2023 period as a new class of compounds for the treatment of AD.

Small Molecule Inhibitors Targeting the "Undruggable" Survivin: The Past, Present, and Future from a Medicinal Chemist's Perspective

Survivin, a homodimeric protein and a member of the IAP family, plays a vital function in cell survival and cycle progression by interacting with various proteins and complexes. Its expression is upregulated in cancers but not detectable in normal tissues. Thus, it has been regarded and validated as an ideal cancer target. However, survivin is "undruggable" due to its lack of enzymatic activities or active sites for small molecules to bind/inhibit. Academic and industrial laboratories have explored different strategies to overcome this hurdle over the past two decades, with some compounds advanced into clinical testing. These strategies include inhibiting survivin expression, its interaction with binding partners and homodimerization. Here, we provide comprehensive analyses of these strategies and perspective on different small molecule survivin inhibitors to help drug discovery targeting "undruggable" proteins in general and survivin specifically with a true survivin inhibitor that will prevail in the foreseeable future.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R² = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

Molecular docking analysis of novel quercetin derivatives for combating SARS-CoV-2

Quercetin belongs to the flavonoid family, which is one of the most frequent types of plant phenolics. This flavonoid compound is a natural substance having a number of pharmacological effects, including anticancer and antioxidant capabilities, as well as being a strong inhibitor of various toxicologically important enzymes. We discuss the potential of newly recently synthesized quercetin-based derivatives to inhibit SARS-CoV-2 protein. ADMET analysis indicated that all of the studied compounds had low toxicities and good absorption and solubility properties. The molecular docking results revealed that the propensity for binding to SARS-CoV-2 main protease is extraordinary. The results are remarkable not only for the binding energy values, which outperform several compounds in prior studies, but also for the number of hydrogen bonds formed. Compound 7a was capable of forming 10 strong hydrogen bonds as well as interact to the protein receptor with a binding energy of -7.79 kcal/mol. Therefore, these compounds should be highlighted in further experimental studies in the context of treating SARS-CoV-2 infection and its effects.

Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease

SARS-CoV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdowns, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure COVID-19. In the first step, a total of 133 drug-likeness bioactive molecules are retrieved from the ChEMBL database against SARS coronavirus 3CL Protease. Based on the standard IC50, the dataset is divided into three classes: active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR)-based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting-, XGBoost-, Support Vector-, Decision Tree-, and Random Forest-based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with the ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134, and 426898. These molecules are highly suitable drug candidates for SARS-CoV-2 3CL Protease. In the next step, the efficacy of the bioactive molecules is computed in terms of binding affinity using molecular docking, and then six bioactive molecules are shortlisted, with the ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-CoV-2. It is anticipated that the pharmacologist and/or drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-CoV-2. They can adopt these promising compounds for their downstream drug development stages.