Why Decreasing Lipophilicity Alone Is Often Not a Reliable Strategy for Extending IV Half-life

Scaffold hopping approaches for dual-target antitumor drug discovery: opportunities and challenges

INTRODUCTION

Scaffold hopping has emerged as a practical tactic to enrich the synthetic bank of small molecule antitumor agents. Specifically, it enables the chemist to refine the lead compound's pharmacodynamic, pharmacokinetic, and physiochemical properties. Scaffold hopping opens up fresh molecular territory beyond established patented chemical domains.

AREA COVERED

The authors present the scaffold hopping-based drug design strategies for dual inhibitory antitumor structural templates in this review. Minor modifications, structure rigidification and simplification (ring-closing and opening), and complete structural overhauls were the strategies employed by the medicinal chemist to generate a library of bifunctional inhibitors. In addition, the review presents an overview of the computational methods of scaffold hopping (software and programs) and organopalladium catalysis leveraged for the synthesis of templates designed via scaffold hopping.

EXPERT OPINION

The medicinal chemist has demonstrated remarkable prowess in furnishing dual inhibitory antitumor chemical architectures. Scaffold hopping-based drug design strategies have yielded a plethora of pharmacodynamically superior dual modulatory antitumor agents. An integrated approach involving computational advancements, synthetic methodology advancements, and conventional drug design strategies is required to increase the number of scaffold-hopping-assisted drug discovery campaigns.

The discovery of novel and potent indazole NLRP3 inhibitors enabled by DNA-encoded library screening

NLRP3 is an intracellular sensor protein that detects a broad range of danger signals and environmental insults. Its activation results in a protective pro-inflammatory response designed to impair pathogens and repair tissue damage via the formation of the NLRP3 inflammasome. Assembly of the NLRP3 inflammasome leads to caspase 1-dependent secretory release of the pro-inflammatory cytokines IL-1β and IL-18 as well as to gasdermin d-mediated pyroptotic cell death. Herein, we describe the discovery of a novel indazole series of high affinity, reversible inhibitors of NLRP3 activation through screening of DNA-encoded libraries and the potent lead compound 3 (BAL-0028, IC50 = 25 nM) that was identified directly from the screen. SPR studies showed that compound 3 binds tightly (KD range 104-123 nM) to the NACHT domain of NLRP3. A CADD analysis of the interaction of compound 3 with the NLRP3 NACHT domain proposes a binding site that is distinct from those of ADP and MCC950 and includes specific site interactions. We anticipate that compound 3 (BAL-0028) and other members of this novel indazole class of neutral inhibitors will demonstrate significantly different physical, biochemical, and biological properties compared to NLRP3 inhibitors previously identified.

Discovery and Optimization of Selective Brain-Penetrant EBP Inhibitors that Enhance Oligodendrocyte Formation

The inhibition of emopamil binding protein (EBP), a sterol isomerase within the cholesterol biosynthesis pathway, promotes oligodendrocyte formation, which has been proposed as a potential therapeutic approach for treating multiple sclerosis. Herein, we describe the discovery and optimization of brain-penetrant, orally bioavailable inhibitors of EBP. A structure-based drug design approach from literature compound 1 led to the discovery of a hydantoin-based scaffold, which provided balanced physicochemical properties and potency and an improved in vitro safety profile. The long half-lives of early hydantoin-based EBP inhibitors in rodents prompted an unconventional optimization strategy, focused on increasing metabolic turnover while maintaining potency and a brain-penetrant profile. The resulting EBP inhibitor 11 demonstrated strong in vivo target engagement in the brain, as illustrated by the accumulation of EBP substrate zymostenol after repeated dosing. Furthermore, compound 11 enhanced the formation of oligodendrocytes in human cortical organoids, providing additional support for our therapeutic hypothesis.

In Vitro Cytotoxic Effects of Ferruginol Analogues in Sk-MEL28 Human Melanoma Cells

Ferruginol is a promising abietane-type antitumor diterpene able to induce apoptosis in SK-Mel-28 human malignant melanoma. We aim to increase this activity by testing the effect of a small library of ferruginol analogues. After a screening of their antiproliferative activity (SRB staining, 48 h) on SK-Mel-28 cells the analogue 18-aminoferruginol (GI50 ≈ 10 µM) was further selected for mechanistic studies including induction of apoptosis (DAPI staining, p < 0.001), changes in cell morphology associated with the treatment (cell shrinkage and membrane blebbing), induction of caspase-3/7 activity (2.5 at 48 h, 6.5 at 72 h; p < 0.0001), changes in the mitochondrial membrane potential (not significant) and in vitro effects on cell migration and cell invasion (Transwell assays, not significant). The results were compared to those of the parent molecule (ferruginol, GI50 ≈ 50 µM, depolarisation of mitochondrial membrane p < 0.01 at 72 h; no caspases 3/7 activation) and paclitaxel (GI50 ≈ 10 nM; caspases 3/7 activation p < 0.0001) as a reference drug. Computational studies of the antiproliferative activity of 18-aminoferruginol show a consistent improvement in the activity over ferruginol across a vast majority of cancer cells in the NCI60 panel. In conclusion, we demonstrate here that the derivatisation of ferruginol into 18-aminoferruginol increases its antiproliferative activity five times in SK-MEL-28 cells and changes the apoptotic mechanism of its parent molecule, ferruginol.

3-Methoxy-2-phenylimidazo[1,2-b]pyridazines highly active against Mycobacterium tuberculosis and Mycobacterium marinum

A series of 3-methoxy-2-phenylimidazo[1,2-b]pyridazine derivatives which were highly active against autoluminescent Mycobacterium tuberculosis (Mtb) and Mycobacterium marinum (Mm) in an in vitro assay were identified. SAR analysis showed that the most active compounds, which included a phenyl group bearing fluoro substituent(s) at C2, a methoxy function at C3, and a benzyl-heteroatom moiety at C6, exhibited in vitro MIC90 values generally around 0.63-1.26 μM against Mtb and Mm. However, these compounds were inactive against Mtb in vivo (mice), and investigations revealed very short metabolic half-lives (<10 min) when incubated with mouse liver microsomes. Multiple observations of side products produced from oxidative cleavage of the imidazole moiety during the chemical synthesis work suggested that this is a likely metabolic pathway leading to the lack of observed activity in vivo.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.

Filling a nick in NIK: extending the half-life of a NIK inhibitor through structure-based drug design

Inhibition of NF-κB inducing kinase (NIK) has been pursued as a promising therapeutic target for autoimmune disorders due to its highly regulated role in key steps of the NF-κB signaling pathway. Previously reported NIK inhibitors from our group were shown to be potent, selective, and efficacious, but had higher human dose projections than desirable for immunology indications. Herein we report the clearance-driven optimization of a NIK inhibitor guided by metabolite identification studies and structure-based drug design. This led to the identification of an azabicyclo[3.1.0]hexanone motif that attenuated in vitro and in vivo clearance while maintaining NIK potency and increasing selectivity over other kinases, resulting in a greater than ten-fold reduction in predicted human dose.

Theoretical and Experimental Study of Molecular Interactions of Fluralaner with Lipid Membranes

Fluralaner is a relatively new insecticide belonging to the isoxazoline group, whose action mechanism involves the blocking of GABA_A-receptors in the insect nervous system. Because of its high hydrophobicity, fluralaner could bioaccumulate and reach toxic local concentrations. Since there are no data available about the penetration and persistence of isoxazolines in biological membranes, we intend to evaluate fluralaner permanence as a pollutant by using model membranes. We used experimental and in silico models to characterize the incorporation of fluralaner into the lipid phase at different packing states. We determined its impact in the membrane structure and organization. Our results confirm that fluralaner is capable of penetrating, holding, and accumulating in the lipid membrane and provide details on its precise location and orientation. These properties would allow fluralaner to reach high local concentrations in different membranes and organs, which could be dangerous for vertebrate organisms if its handling is not properly controlled.

Selective PROTAC-mediated degradation of SMARCA2 is efficacious in SMARCA4 mutant cancers

The mammalian SWItch/Sucrose Non-Fermentable (SWI/SNF) helicase SMARCA4 is frequently mutated in cancer and inactivation results in a cellular dependence on its paralog, SMARCA2, thus making SMARCA2 an attractive synthetic lethal target. However, published data indicates that achieving a high degree of selective SMARCA2 inhibition is likely essential to afford an acceptable therapeutic index, and realizing this objective is challenging due to the homology with the SMARCA4 paralog. Herein we report the discovery of a potent and selective SMARCA2 proteolysis-targeting chimera molecule (PROTAC), A947. Selective SMARCA2 degradation is achieved in the absence of selective SMARCA2/4 PROTAC binding and translates to potent in vitro growth inhibition and in vivo efficacy in SMARCA4 mutant models, compared to wild type models. Global ubiquitin mapping and proteome profiling reveal no unexpected off-target degradation related to A947 treatment. Our study thus highlights the ability to transform a non-selective SMARCA2/4-binding ligand into a selective and efficacious in vivo SMARCA2-targeting PROTAC, and thereby provides a potential new therapeutic opportunity for patients whose tumors contain SMARCA4 mutations.

Membrane Lipids Are an Integral Part of Transmembrane Allosteric Sites in GPCRs: A Case Study of Cannabinoid CB1 Receptor Bound to a Negative Allosteric Modulator, ORG27569, and Analogs

A growing number of G-protein-coupled receptor (GPCR) structures reveal novel transmembrane lipid-exposed allosteric sites. Ligands must first partition into the surrounding membrane and take lipid paths to these sites. Remarkably, a significant part of the bound ligands appears exposed to the membrane lipids. The experimental structures do not usually account for the surrounding lipids, and their apparent contribution to ligand access and binding is often overlooked and poorly understood. Using classical and enhanced molecular dynamics simulations, we show that membrane lipids are critical in the access and binding of ORG27569 and its analogs at the transmembrane site of cannabinoid CB1 receptor. The observed differences in the binding affinity and cooperativity arise from the functional groups that interact primarily with lipids. Our results demonstrate the significance of incorporating membrane lipids as an integral component of transmembrane sites for accurate characterization, binding-affinity calculations, and lead optimization in drug discovery.

LipMetE (Lipophilic Metabolism Efficiency) as a Simple Guide for Half-Life and Dosing Regimen Prediction of Oral Drugs

The in vivo half-life is a key property of every drug molecule, as it determines dosing regimens, peak-to-trough ratios and often dose. However, half-life optimization can be challenging due to its multifactorial nature, with in vitro metabolic turnover, plasma protein binding and volume of distribution all impacting half-life. We here propose that the medicinal chemistry design parameter Lipophilic Metabolism Efficiency (LipMetE) can greatly simplify half-life optimization of neutral and basic compounds. Using mathematical transformations, examples from preclinical GABAA projects and clinical compounds with human pharmacokinetic data, we show that LipMetE is directly proportional to the logarithm of half-life. As the design parameter LipMetE can be swiftly calculated using the readily available parameters LogD, intrinsic clearance and fraction unbound in human liver microsomes or hepatocytes, this approach enables rational half-life optimization from the early stages of drug discovery projects.

Development of New Antimicrobial Oleanonic Acid Polyamine Conjugates

A series of oleanolic acid derivatives holding oxo- or 3-N-polyamino-3-deoxy-substituents at C3 as well as carboxamide function at C17 with different long chain polyamines have been synthesized and evaluated for antimicrobial activities. Almost all series presented good to moderate activity against Gram-positive S. aureus, S. faecalis and B. cereus bacteria with minimum inhibitory concentration (MIC) values from 3.125 to 200 µg/mL. Moreover, compounds possess important antimicrobial activities against Gram-negative E. coli, P. aeruginosa, S. enterica, and EA289 bacteria with MICs ranging from 6.25 to 200 µg/mL. The testing of ability to restore antibiotic activity of doxycycline and erythromycin at a 2 µg/mL concentration in a synergistic assay showed that oleanonic acid conjugate with spermine spacered through propargylamide led to a moderate improvement in terms of antimicrobial activities of the different selected combinations against both P. aeruginosa and E. coli. The study of mechanism of action of the lead conjugate 2i presenting a N-methyl norspermidine moiety showed the effect of disruption of the outer bacterial membrane of P. aeruginosa PA01 cells. Computational ADMET profiling renders this compound as a suitable starting point for pharmacokinetic optimization. These results give confidence to the successful outcome of bioconjugation of polyamines and oleanane-type triterpenoids in the development of antimicrobial agents.

Molecule Ideation Using Matched Molecular Pairs

Matched Molecular Pair Analysis (MMP) is a very important tool during the lead optimization stage in drug discovery. The usefulness of this tool in the lead optimization stage has been discussed in several peer-reviewed articles. The application of MMP in Molecule generation is relatively new. This brings several challenges one of them being the need to encode contextual information into the transforms. In this chapter, we discuss how we use MMPs as a molecule generation method and how does it compare with other molecular generators.

A Retrospective Look at the Impact of Binding Site Environment on the Optimization of TRPA1 Antagonists

Transient receptor potential ankyrin 1 (TRPA1) antagonists have generated broad interest in the pharmaceutical industry for the treatment of both pain and asthma. Over the past decade, multiple antagonist classes have been reported in the literature with a wide range of structural diversity. Our own work has focused on the development of proline sulfonamide and hypoxanthine-based antagonists, two antagonist classes with distinct physicochemical properties and pharmacokinetic (PK) trends. Late in our discovery program, cryogenic electron microscopy (cryoEM) studies revealed two different antagonist binding sites: a membrane-exposed proline sulfonamide transmembrane site and an intracellular hypoxanthine site near the membrane interface. A retrospective look at the discovery program reveals how the different binding sites, and their location relative to the cell membrane, influenced the optimization trajectories and overall drug profiles of each antagonist class.

Ligand binding at the protein-lipid interface: strategic considerations for drug design

Many drug targets are embedded within the phospholipid bilayer of cellular membranes, including G protein-coupled receptors, ion channels, transporters and membrane-bound enzymes. Increasing evidence from biophysical and structural studies suggests that many small-molecule drugs commonly associate with these targets at binding sites at the protein-phospholipid interface. Without a direct path from bulk solvent to a binding site, a drug must first partition in the phospholipid membrane before interacting with the protein target. This membrane access mechanism necessarily affects the interpretation of potency data, structure-activity relationships, pharmacokinetics and physicochemical properties for drugs that target these sites. With an increasing number of small-molecule intramembrane binding sites revealed through X-ray crystallography and cryogenic electron microscopy, we suggest that ligand-lipid interactions likely play a larger role in small-molecule drug action than commonly appreciated. This Perspective introduces key concepts and drug design considerations to aid discovery teams operating within this target space, and discusses challenges and future opportunities in the field.

Evaluation of Tissue Binding in Three Tissues across Five Species and Prediction of Volume of Distribution from Plasma Protein and Tissue Binding with an Existing Model

Volume of distribution (V_d) is a primary pharmacokinetic parameter used to calculate the half-life and plasma concentration-time profile of drugs. Numerous models have been relatively successful in predicting V_d, but the model developed by Korzekwa and Nagar is of particular interest because it utilizes plasma protein binding and microsomal binding data, both of which are readily available in vitro parameters. Here, Korzekwa and Nagar's model was validated and expanded upon using external and internal data sets. Tissue binding, plasma protein binding, V_d, physiochemical, and physiologic data sets were procured from literature and Genentech's internal data base. First, we investigated the hypothesis that tissue binding is primarily governed by passive processes that depend on the lipid composition of the tissue type. The fraction unbound in tissues (fu_tissue) was very similar across human, rat, and mouse. In addition, we showed that dilution factors could be generated from nonlinear regression so that one fu_tissue value could be used to estimate another one regardless of species. More importantly, results suggested that microsomes could serve as a surrogate for tissue binding. We applied the parameters from Korzekwa and Nagar's V_d model to two distinct liver microsomal data sets and found remarkably close statistical results. Brain and lung data sets also accurately predicted V_d, further validating the model. V_d prediction accuracy for compounds with log D_7.4 > 1 significantly outperformed that of more hydrophilic compounds. Finally, human V_d predictions from Korzekwa and Nagar's model appear to be as accurate as rat allometry and slightly less accurate than dog and cynomolgus allometry. SIGNIFICANCE STATEMENT: This study shows that tissue binding is comparable across five species and can be interconverted with a dilution factor. In addition, we applied internal and external data sets to the volume of distribution model developed by Korzekwa and Nagar and found comparable V_d prediction accuracy between the V_d model and single-species allometry. These findings could potentially accelerate the drug research and development process by reducing the amount of resources associated with in vitro binding and animal experiments.

Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX)

The 12th International Society for the Study of Xenobiotics (ISSX) meeting, held in Portland, OR, USA from July 28 to 31, 2019, was attended by diverse members of the pharmaceutical sciences community. The ISSX New Investigators Group provides learning and professional growth opportunities for student and early career members of ISSX. To share meeting content with those who were unable to attend, the ISSX New Investigators herein elected to highlight the "Advances in the Study of Drug Metabolism" symposium, as it engaged attendees with diverse backgrounds. This session covered a wide range of current topics in drug metabolism research including predicting sites and routes of metabolism, metabolite identification, ligand docking, and medicinal and natural products chemistry, and highlighted approaches complemented by computational modeling. In silico tools have been increasingly applied in both academic and industrial settings, alongside traditional and evolving in vitro techniques, to strengthen and streamline pharmaceutical research. Approaches such as quantum mechanics simulations facilitate understanding of reaction energetics toward prediction of routes and sites of drug metabolism. Furthermore, in tandem with crystallographic and orthogonal wet lab techniques for structural validation of drug metabolizing enzymes, in silico models can aid understanding of substrate recognition by particular enzymes, identify metabolic soft spots and predict toxic metabolites for improved molecular design. Of note, integration of chemical synthesis and biosynthesis using natural products remains an important approach for identifying new chemical scaffolds in drug discovery. These subjects, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are discussed in this review.

Quantum Chemical Lipophilicities of Antimalarial Drugs in Relation to Terminal Half-Life

According to the WHO, artemisinin-based combination therapies (ACTs) have been integral to the recent reduction in deaths due to Plasmodium falciparum malaria. ACT-resistant strains are an emerging problem and have evolved altered developmental stages, reducing exposure of the most susceptible stages to artemisinin drugs in popular ACTs. Lipophilicity, log K ow, is a guide in understanding and predicting pharmacokinetic properties such as terminal half-life which alters drug exposure. Consistent log K ow values are not necessarily available for artemisinin derivatives designed to extend terminal half-life, increase bioavailability, and reduce neurotoxicity. For other drugs used in ACTs, an assortment of experimental and computational log K ow values are available in the literature and in some cases, do not account for subtle but important differences between closely related structures such as between diastereomers. Quantum chemical methods such as density functional theory (DFT) used with an implicit solvent model allow for consistent comparison of physical properties including log K ow and distinguish between closely related structures. To this end, DFT, B3LYP/6-31G(d), with an implicit solvent model (SMD) was used to compute ΔG ow o and ΔG vow o for 1-octanol-water and olive oil-water partitions, respectively, for 21 antimalarial drugs: 12 artemisinin-based, 4 4-aminoquinolines and structurally similar pyronaridine, and 4 amino alcohols. The computed ΔG ow o was close to ΔG ow o calculated from experimental log K ow values from the literature where available, with a mean signed error of 2.3 kJ/mol and mean unsigned error of 3.7 kJ/mol. The results allow assignment of log K ow for α-and β-diastereomers of arteether, and prediction of log K ow for β-DHA and five experimental drugs. Linear least square analysis of log K ow and log K vow versus terminal elimination half-life showed strong linear relationships, once the data points for the 4-aminoquinoline drugs, mefloquine and pyronaridine were found to follow their own linear relationship, which is consistent with their different plasma protein binding. The linear relationship between the computed log K vow and terminal elimination half-life was particularly strong, R 2 = 0.99 and F = 467, and can be interpreted in terms of a simple pharmacokinetic model. Terminal elimination half-life for β-DHA and four experimental artemisinin drugs were estimated based on this linear relationship between log K vow and terminal t 1/2. The computed log K ow and log K vow values for epimers α- and β-DHA and α and β-arteether provide physical data that may be helpful in understanding their different pharmacokinetics and activity based on their different molecular geometries. Relative solubility of quinine and quinidine are found to be sensitive to thermal corrections to enthalpy and to vibrational entropy and do not follow the general trend of longer terminal t 1/2 with greater predicted log K ow. Geometric relaxation of α- and β-DHA in solvent and inclusion of thermal correction for enthalpy and entropy results in correct prediction that α-DHA is favored in aqueous environments compared to β-DHA. Predictions made regarding experimental drugs have implications regarding their potential use in response to artemisinin drug-resistant strains.

Scaffold-hopping as a strategy to address metabolic liabilities of aromatic compounds

Understanding and minimizing oxidative metabolism of aromatic compounds is a key hurdle in lead optimization. Metabolic processes not only clear compounds from the body, but they can also transform parent compounds into reactive metabolites. One particularly useful strategy when addressing metabolically labile or oxidation-prone structures is scaffold-hopping. Replacement of an aromatic system with a more electron-deficient ring system can often increase robustness towards cytochrome P450-mediated oxidation while conserving the structural requirements of the pharmacophore. The most common example of this substitution strategy, replacement of a phenyl ring with a pyridyl substituent, is prevalent throughout the literature; however scaffold-hopping encompasses a much wider scope of heterocycle replacement. This review will showcase recent examples where different scaffold-hopping approaches were used to reduce metabolic clearance or block the formation of reactive metabolites. Additionally, we will highlight considerations that should be made to garner the most benefit from a scaffold-hopping strategy for lead optimization.

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 1352 Drug Compounds

We report a trend analysis of human intravenous pharmacokinetic data on a data set of 1352 drugs. The aim in building this data set and its detailed analysis was to provide, as in the previous case published in 2008, an extended, robust, and accurate resource that could be applied by drug metabolism, clinical pharmacology, and medicinal chemistry scientists to a variety of scaling approaches. All in vivo data were obtained or derived from original references, either through the literature or regulatory agency reports, exclusively from studies utilizing intravenous administration. Plasma protein binding data were collected from other available sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of physicochemical properties, and resultant trends and patterns within the data are presented. In addition, the date of first disclosure of each molecule was reported and the potential "temporal" impact on data trends was analyzed. The findings reported here are consistent with earlier described trends between pharmacokinetic behavior and physicochemical properties. Furthermore, the availability of a large data set of pharmacokinetic data in humans will be important to further pursue analyses of physicochemical properties, trends, and modeling efforts and should propel our deeper understanding (especially in terms of clearance) of the absorption, distribution, metabolism, and excretion behavior of drug compounds.