Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties

Comparing the Pfizer Central Nervous System Multiparameter Optimization Calculator and a BBB Machine Learning Model

The ability to calculate whether small molecules will cross the blood-brain barrier (BBB) is an important task for companies working in neuroscience drug discovery. For a decade, scientists have relied on relatively simplistic rules such as Pfizer's central nervous system multiparameter optimization models (CNS-MPO) for guidance during the drug selection process. In parallel, there has been a continued development of more sophisticated machine learning models that utilize different molecular descriptors and algorithms; however, these models represent a "black box" and are generally less interpretable. In both cases, these methods predict the ability of small molecules to cross the BBB using the molecular structure information on its own without in vitro or in vivo data. We describe here the implementation of two versions of Pfizer's algorithm (Pf-MPO.v1 and Pf-MPO.v2) and compare it with a Bayesian machine learning model of BBB penetration trained on a data set of 2296 active and inactive compounds using extended connectivity fingerprint descriptors. The predictive ability of these approaches was compared with 40 known CNS active drugs initially used by Pfizer as their positive set for validation of the Pf-MPO.v1 score. 37/40 (92.5%) compounds were predicted as active by the Bayesian model, while only 30/40 (75%) received a desirable Pf-MPO.v1 score ≥4 and 33/40 (82.5%) received a desirable Pf-MPO.v2 score ≥4, suggesting the Bayesian model is more accurate than MPO algorithms. This also indicates machine learning models are more flexible and have better predictive power for BBB penetration than simple rule sets that require multiple, accurate descriptor calculations. Our machine learning model statistics are comparable to recent published studies. We describe the implications of these findings and how machine learning may have a role alongside more interpretable methods.

Pyrazole-Thiazole Core-Containing Analogs Exhibit Adjunctive Activity with Meropenem against Carbapenem-Resistant Enterobacteriaceae (CRE)

Pyrazole-thiazole core-containing compound KP-40 and 20 novel derivatives were designed and synthesized through traditional SAR analysis. These molecules displayed adjunctive activity with meropenem against Gram-negative bacteria evidenced by a range of fractional inhibitory concentration (FIC=0.5-0.25) and minimum adjunctive concentration (MAC=128-32 μM) values. Of this series of molecules, four compounds displayed notable adjunctive potential, with FIC and MAC values of 0.25 and 32 μM, respectively. Moreover, the solubility of these compounds was improved to an acceptable range. Further analysis using our "in house" permeation and efflux multi parameter optimization (PEMPO) algorithm revealed key physicochemical properties that may be critical for the development of active Gram-negative antibacterials. Taking PEMPO scores into consideration prior to executing synthesis of analogs may be a simple, yet rapid and effective strategy that can be used in conjunction with traditional SAR approaches to aid in the design of potent Gram-negative antibacterials.

Design, synthesis, and biological evaluation of furosemide analogs as therapeutics for the proteopathy and immunopathy of Alzheimer's disease

β-Amyloid (Aβ) triggered proteopathic and immunopathic processes are a postulated cause of Alzheimer's disease (AD). Monomeric Aβ is derived from amyloid precursor protein, whereupon it aggregates into various assemblies, including oligomers and fibrils, which disrupt neuronal membrane integrity and induce cellular damage. Aβ is directly neurotoxic/synaptotoxic, but may also induce neuroinflammation through the concomitant activation of microglia. Previously, we have shown that furosemide is a known anthranilate-based drug with the capacity to downregulate the proinflammatory microglial M1 phenotype and upregulate the anti-inflammatory M2 phenotype. To further explore the pharmacologic effects of furosemide, this study reports a series of furosemide analogs that target both Aβ aggregation and neuroinflammation, thereby addressing the combined proteopathic-immunopathic pathogenesis of AD. Forty compounds were synthesized and evaluated. Compounds 3c, 3g, and 20 inhibited Aβ oligomerization; 33 and 34 inhibited Aβ fibrillization. 3g and 34 inhibited the production of TNF-α, IL-6, and nitric oxide, downregulated the expression of COX-2 and iNOS, and promoted microglial phagocytotic activity, suggesting dual activity against Aβ aggregation and neuroinflammation. Our data demonstrate the potential therapeutic utility of the furosemide-like anthranilate platform in the development of drug-like molecules targeting both the proteopathy and immunopathy of AD.

Novel Dual-Target μ-Opioid Receptor and Dopamine D3 Receptor Ligands as Potential Nonaddictive Pharmacotherapeutics for Pain Management

The need for safer pain-management therapies with decreased abuse liability inspired a novel drug design that retains μ-opioid receptor (MOR)-mediated analgesia, while minimizing addictive liability. We recently demonstrated that targeting the dopamine D₃ receptor (D₃R) with highly selective antagonists/partial agonists can reduce opioid self-administration and reinstatement to drug seeking in rodent models without diminishing antinociceptive effects. The identification of the D₃R as a target for the treatment of opioid use disorders prompted the idea of generating a class of ligands presenting bitopic or bivalent structures, allowing the dual-target binding of the MOR and D₃R. Structure-activity relationship studies using computationally aided drug design and in vitro binding assays led to the identification of potent dual-target leads (23, 28, and 40), based on different structural templates and scaffolds, with moderate (sub-micromolar) to high (low nanomolar/sub-nanomolar) binding affinities. Bioluminescence resonance energy transfer-based functional studies revealed MOR agonist-D₃R antagonist/partial agonist efficacies that suggest potential for maintaining analgesia with reduced opioid-abuse liability.

Discovery of a Necroptosis Inhibitor Improving Dopaminergic Neuronal Loss after MPTP Exposure in Mice

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, mainly characterized by motor deficits correlated with progressive dopaminergic neuronal loss in the substantia nigra pars compacta (SN). Necroptosis is a caspase-independent form of regulated cell death mediated by the concerted action of receptor-interacting protein 3 (RIP3) and the pseudokinase mixed lineage domain-like protein (MLKL). It is also usually dependent on RIP1 kinase activity, influenced by further cellular clues. Importantly, necroptosis appears to be strongly linked to several neurodegenerative diseases, including PD. Here, we aimed at identifying novel chemical inhibitors of necroptosis in a PD-mimicking model, by conducting a two-step screening. Firstly, we phenotypically screened a library of 31 small molecules using a cellular model of necroptosis and, thereafter, the hit compound effect was validated in vivo in a sub-acute 1-methyl-1-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP) PD-related mouse model. From the initial compounds, we identified one hit—Oxa12—that strongly inhibited necroptosis induced by the pan-caspase inhibitor zVAD-fmk in the BV2 murine microglia cell line. More importantly, mice exposed to MPTP and further treated with Oxa12 showed protection against MPTP-induced dopaminergic neuronal loss in the SN and striatum. In conclusion, we identified Oxa12 as a hit compound that represents a new chemotype to tackle necroptosis. Oxa12 displays in vivo effects, making this compound a drug candidate for further optimization to attenuate PD pathogenesis.

Toward the assembly and characterization of an encoded library hit confirmation platform: Bead-Assisted Ligand Isolation Mass Spectrometry (BALI-MS)

The ability to predict chemical structure from DNA sequence has to date been a necessary cornerstone of DNA-encoded library technology. DNA-encoded libraries (DELs) are typically screened by immobilized affinity selection and enriched library members are identified by counting the number of times an individual compound's sequence is observed in the resultant dataset. Those with high signal reads (DEL hits) are subsequently followed up through off-DNA synthesis of the predicted small molecule structures. However, hits followed-up in this manner often fail to translate to confirmed ligands. To address this low conversion rate of DEL hits to off-DNA ligands, we have developed an approach that eliminates the reliance on chemical structure prediction from DNA sequence. Here we describe our method of combining non-combinatorial resynthesis on-DNA following library procedures as a rapid means to assess the probable molecules attached to the DNA barcode. Furthermore, we apply our Bead-Assisted Ligand Isolation Mass Spectrometry (BALI-MS) technique to identify the true binders found within the mixtures of on-DNA synthesis products. Finally, we describe a Normalized Enrichment (NE) metric that allows for the quantitative assessment of affinity selection in these studies. We exemplify how this combined approach enables the identification of putative hit matter against a clinically relevant therapeutic target bisphosphoglycerate mutase, BPGM.

Development of a Robust High-Throughput Screening Platform for Inhibitors of the Striatal-Enriched Tyrosine Phosphatase (STEP)

Many human diseases are the result of abnormal expression or activation of protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs). Not surprisingly, more than 30 tyrosine kinase inhibitors (TKIs) are currently in clinical use and provide unique treatment options for many patients. PTPs on the other hand have long been regarded as “undruggable” and only recently have gained increased attention in drug discovery. Striatal-enriched tyrosine phosphatase (STEP) is a neuron-specific PTP that is overactive in Alzheimer’s disease (AD) and other neurodegenerative and neuropsychiatric disorders, including Parkinson’s disease, schizophrenia, and fragile X syndrome. An emergent model suggests that the increase in STEP activity interferes with synaptic function and contributes to the characteristic cognitive and behavioral deficits present in these diseases. Prior efforts to generate STEP inhibitors with properties that warrant clinical development have largely failed. To identify novel STEP inhibitor scaffolds, we developed a biophysical, label-free high-throughput screening (HTS) platform based on the protein thermal shift (PTS) technology. In contrast to conventional HTS using STEP enzymatic assays, we found the PTS platform highly robust and capable of identifying true hits with confirmed STEP inhibitory activity and selectivity. This new platform promises to greatly advance STEP drug discovery and should be applicable to other PTP targets.

Tricyclic Triazoles as σ1 Receptor Antagonists for Treating Pain

The synthesis and pharmacological activity of a new series of 5a,7,8,8a-tetrahydro-4H,6H-pyrrolo[3,4-b][1,2,3]triazolo[1,5-d][1,4]oxazine derivatives as potent sigma-1 receptor (σ1R) ligands are reported. A lead optimization program aimed at improving the aqueous solubility of parent racemic nonpolar derivatives led to the identification of several σ1R antagonists with a good absorption, distribution, metabolism, and excretion in vitro profile, no off-target affinities, and characterized by a low basic pKa (around 5) that correlates with high exposure levels in rodents. Two compounds displaying a differential brain-to-plasma ratio distribution profile, 12lR and 12qS, exhibited a good analgesic profile and were selected as preclinical candidates for the treatment of pain.

Repurposing Immunomodulatory Imide Drugs (IMiDs) in Neuropsychiatric and Neurodegenerative Disorders

Neuroinflammation represents a common trait in the pathology and progression of the major psychiatric and neurodegenerative disorders. Neuropsychiatric disorders have emerged as a global crisis, affecting 1 in 4 people, while neurological disorders are the second leading cause of death in the elderly population worldwide (WHO, 2001; GBD 2016 Neurology Collaborators, 2019). However, there remains an immense deficit in availability of effective drug treatments for most neurological disorders. In fact, for disorders such as depression, placebos and behavioral therapies have equal effectiveness as antidepressants. For neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, drugs that can prevent, slow, or cure the disease have yet to be found. Several non-traditional avenues of drug target identification have emerged with ongoing neurological disease research to meet the need for novel and efficacious treatments. Of these novel avenues is that of neuroinflammation, which has been found to be involved in the progression and pathology of many of the leading neurological disorders. Neuroinflammation is characterized by glial inflammatory factors in certain stages of neurological disorders. Although the meta-analyses have provided evidence of genetic/proteomic upregulation of inflammatory factors in certain stages of neurological disorders. Although the mechanisms underpinning the connections between neuroinflammation and neurological disorders are unclear, and meta-analysis results have shown high sensitivity to factors such as disorder severity and sample type, there is significant evidence of neuroinflammation associations across neurological disorders. In this review, we summarize the role of neuroinflammation in psychiatric disorders such as major depressive disorder, generalized anxiety disorder, post-traumatic stress disorder, and bipolar disorder, as well as in neurodegenerative disorders, such as Parkinson's disease and Alzheimer's disease, and introduce current research on the potential of immunomodulatory imide drugs (IMiDs) as a new treatment strategy for these disorders.

Harnessing Preclinical Data as a Predictive Tool for Human Brain Tissue Targeting

One of the objectives within the medicinal chemistry discipline is to design tissue targeting molecules. The objective of tissue specificity can be either to gain drug access to the compartment of interest (e.g., the CNS) for Neuroscience targets or to restrict drug access to the CNS for all other therapeutic areas. Both neuroscience and non-neuroscience therapeutic areas have struggled to quantitatively estimate brain penetration or the lack thereof with compounds that are substrates of efflux transport proteins such as P-glycoprotein (P-gp) and breast cancer resistant protein (BCRP) that are key components of the blood-brain barrier (BBB). It has been well established that drug candidates with high efflux ratios (ER) of these transporters have poor penetration into brain tissue. In the current work, we outline a parallel analysis to previously published models for the prediction of brain penetration that utilize an alternate MDR1-MDCK cell line as a better predictor of brain penetration and whether a correlation between in vitro, rodent data, non-human primate (NHP), and human in vivo brain penetration data could be established. Analysis of structural and physicochemical properties in conjunction with in vitro parameters and preclinical in vivo data has been highlighted in this manuscript as a continuation of the previously published work.

Optimization of dose and route of administration of the P-glycoprotein inhibitor, valspodar (PSC-833) and the P-glycoprotein and breast cancer resistance protein dual-inhibitor, elacridar (GF120918) as dual infusion in rats

Transporters can play a key role in the absorption, distribution, metabolism, and excretion of drugs. Understanding these contributions early in drug discovery allows for more accurate projection of the clinical pharmacokinetics. One method to assess the impact of transporters in vivo involves co‐dosing specific inhibitors. The objective of the present study was to optimize the dose and route of administration of a P‐glycoprotein (P‐gp) inhibitor, valspodar (PSC833), and a dual P‐gp/breast cancer resistance protein (BCRP) inhibitor, elacridar (GF120918), by assessing the transporters’ impact on brain penetration and absorption. A dual‐infusion strategy was implemented to allow for flexibility with dose formulation. The chemical inhibitor was dosed intravenously via the femoral artery, and a cassette of known substrates was infused via the jugular vein. Valspodar or elacridar was administered as 4.5‐hour constant infusions over a range of doses. To assess the degree of inhibition, the resulting ratios of brain and plasma concentrations, Kp's, of the known substrates were compared to the vehicle control. These data demonstrated that doses greater than 0.9 mg/hr/kg valspodar and 8.9 mg/hr/kg elacridar were sufficient to inhibit P‐gp‐ and BCRP‐mediated efflux at the blood‐brain barrier in rats without any tolerability issues. Confirmation of BBB restriction by efflux transporters in preclinical species allows for subsequent prediction in humans based upon the proteomic expression at rodent and human BBB. Overall, the approach can also be applied to inhibition of efflux at other tissues (gut absorption, liver clearance) or can be extended to other transporters of interest using alternate inhibitors.

N,N′-dialkyl-2-thiobarbituric acid based sulfonamides as potential SARS-CoV-2 main protease inhibitors

An efficient methodology was developed to generate novel N,N′-dialkyl-2-thiobarbituric acid based sulfonamides S1–S4 in good to excellent yields (84%–95%). The synthesized compounds S1–S4 were docked to screen their in silico activities against two enzymes i.e., SARS-CoV-2 main protease enzyme with unliganded active site (2019-nCoV, coronavirus disease 2019, COVID-19) PDB ID: 6Y84 and SARS-CoV-2 Mpro PDB ID: 6LU7. Furthermore, some in silico physicochemical and physicokinetic properties were evaluated using the OSIRIS Property Explorer, Molinspiration property calculator, ADMET property calculator, and GUSAR to assess these compounds as potential candidates as lead compounds for the quest of SARS-CoV-2 main protease inhibitors. Molecular docking analyses of the synthesized compounds predicted that compound S3 is more potent as SARS-CoV-2 main protease inhibitor with binding energy –11.65 kcal/mol in comparison with reference inhibitor N3 (–10.95 kcal/mol), whereas compounds S1, S2, and S4 recorded comparable binding energies –9.89, –10.84, and –10.94 kcal/mol with reference inhibitor N3, which were much better than remdesivir (–9.85 kcal/mol). In case of SARS-CoV-2 Mpro, all compounds S1–S4 with docking energy values of –7.28, –8.38, –8.31, and –7.34 kcal/mol, respectively, were found to be potent in comparison with reference inhibitor N3 (–6.31 kcal/mol) and remdesivir (–6.33 kcal/mol). Ligand efficiency values against the target SARS-CoV-2 proteins, as well as α-glucosidase and DNA-(apurinic or apyrimidinic site) lyase inhibition results of these newly synthesized compounds, were also found to be promising.

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and de novo molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, hERG channel inhibition, passive permeability, and cytochrome P450 inhibition. The proposed methodology highlighted molecular features and structural elements that are in agreement with known pharmacophore motifs, correctly identified property cliffs, and provided insights into unspecific ligand-target interactions. The developed XAI approach is fully open-sourced and can be used by practitioners to train new models on other clinically relevant endpoints.

Radiofluorination of oxazole-carboxamides for preclinical PET neuroimaging of GSK-3

Glycogen synthase kinase 3 (GSK-3) is an enzyme that is dysregulated in oncology neurodegeneration, neuroinflammation and several mental health illnesses. As such, GSK-3 is a long-sought after target for positron emission tomography (PET) imaging and therapeutic intervention. Herein, we report on the development and radiofluorination of two oxazole-4-carboxamides, including one bearing a non-activated aromatic ring. Both compounds demonstrated excellent selectivity in a kinase screen and inhibit GSK-3 with high affinity. [¹⁸F]OCM-49 was synthesized from [¹⁸F]fluoride using a copper-mediated reaction of an aryl boronic acid precursor, while [¹⁸F]OCM-50 used a trimethylammonium triflate precursor, and both radiotracers were translated for preclinical PET imaging in rodents. Due to superior radiochemical yields and brain uptake (peak standardized uptake value of ~2.0), [¹⁸F]OCM-50 was further evaluated in non-human primate and also showed good brain uptake and rapid clearance. Further studies to consider clinical translation of both radiotracers are underway.

Radiosynthesis, In Vitro and In Vivo Evaluation of [18F]CBD-2115 as a First-in-Class Radiotracer for Imaging 4R-Tauopathies

CBD-2115 was selected from a library of 148 compounds based on a pyridinyl-indole scaffold as a first-in-class 4R-tau radiotracer. In vitro binding assays showed [³H]CBD-2115 had a K_D value of 6.9 nM and a nominal B_max of 500 nM in 4R-tau expressing P301L transgenic mouse tissue. In binding assays with human brain tissue homogenates, [³H]CBD-2115 has a higher affinity (4.9 nM) for progressive supranuclear palsy specific 4R-tau deposits than [³H]flortaucipir (45 nM) or [³H]MK-6240 (>50 nM). [¹⁸F]CBD-2115 was reliably synthesized (3-11% radiochemical yield with molar activity of 27-111 GBq/μmol and >97% radiochemical purity). Dynamic PET imaging was conducted in mice, rats, and nonhuman primates, and all species showed initial brain uptake of 0.5-0.65 standardized uptake value with fast clearance from normal tissues. [³H]CBD-2115 could be a useful lead radioligand for further research in 4R-tauopathies, and PET radiotracer development will focus on improving brain uptake and binding affinity.

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1

Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1. This enzyme catalyzes the removal of sialic acid moieties from glycoproteins and glycolipids. Therefore, the defective or deficient protein leads to the buildup of sialylated glycoproteins as well as several characteristic symptoms of sialidosis including visual impairment, ataxia, hepatomegaly, dysostosis multiplex, and developmental delay. In this study, we used a bibliometric tool to generate links between lysosomal storage disease (LSD) targets and existing bioactivity data that could be curated in order to build machine learning models and screen compounds in silico. We focused on sialidase as an example, and we used the data curated from the literature to build a Bayesian model which was then used to score compound libraries and rank these molecules for in vitro testing. Two compounds were identified from in vitro testing using microscale thermophoresis, namely sulfameter (K d 2.15 ± 1.02 μM) and mexenone (K d 8.88 ± 4.02 μM), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.

BK Channels Regulate LPS-induced CCL-2 Release from Human Pulmonary Endothelial Cells

We recently established a role for the stretch-activated two-pore-domain K+ (K2P) channel TREK-1 (K2P2.1) in inflammatory cytokine secretion using models of hyperoxia-, mechanical stretch-, and TNF-α-induced acute lung injury. We have now discovered the expression of large conductance, Ca2+-activated K+ (BK) channels in human pulmonary microvascular endothelial cells and primary human alveolar epithelial cells using semiquantitative real-time PCR, IP and Western blot, and investigated their role in inflammatory cytokine secretion using an LPS-induced acute lung injury model. As expected, LPS induced IL-6 and CCL-2 secretion from pulmonary endothelial and epithelial cells. BK activation with NS1619 decreased LPS-induced CCL-2 but not IL-6 secretion from endothelial cells and had no effect on epithelial cells, although fluorometric assays revealed that BK activation hyperpolarized the plasma membrane potential (Em) of both cell types. Interestingly, BK inhibition (Paxilline) did not alter cytokine secretion or the Em in either cell type. Furthermore, LPS treatment by itself did not affect the Em or intracellular Ca2+ concentrations. Therefore, we propose BK channel activation as a novel targeted approach to counteract LPS-induced CCL-2 secretion from endothelial cells. This protective effect appears to occur via Em hyperpolarization but independent of intracellular Ca2+ concentrations.

Discovery of PIPE-359, a Brain-Penetrant, Selective M1 Receptor Antagonist with Robust Efficacy in Murine MOG-EAE

The discovery of PIPE-359, a brain-penetrant and selective antagonist of the muscarinic acetylcholine receptor subtype 1 is described. Starting from a literature-reported M₁ antagonist, linker replacement and structure-activity relationship investigations of the eastern 1-(pyridinyl)piperazine led to the identification of a novel, potent, and selective antagonist with good MDCKII-MDR1 permeability. Continued semi-iterative positional scanning facilitated improvements in the metabolic and hERG profiles, which ultimately delivered PIPE-359. This advanced drug candidate exhibited robust efficacy in mouse myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalitis (EAE), a preclinical model for multiple sclerosis.

Discovery of a Novel Series of Potent and Selective Alkynylthiazole-Derived PI3Kγ Inhibitors

Phosphoinositide 3-kinases (PI3Ks) are a family of enzymes that control a wide variety of cellular functions such as cell growth, proliferation, differentiation, motility, survival, and intracellular trafficking. PI3Kγ plays a critical role in mediating leukocyte chemotaxis as well as mast cell degranulation, making it a potentially interesting target for autoimmune and inflammatory diseases. We previously disclosed a novel series of PI3Kγ inhibitors derived from a benzothiazole core. The truncation of the benzothiazole core led to the discovery of a structurally diverse alkynyl thiazole series which displayed high PI3Kγ potency and subtype selectivity. Further medicinal chemistry optimization of the alkynyl thiazole series led to identification of compounds such as 14 and 32, highly potent, subtype selective, and CNS penetrant PI3Kγ inhibitors. Compound 14 showed robust inhibition of PI3Kγ mediated neutrophil migration in vivo.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

Selecting Approaches for Hit Identification and Increasing Options by Building the Efficient Discovery of Actionable Chemical Matter from DNA-Encoded Libraries

Over the past 20 years, the toolbox for discovering small-molecule therapeutic starting points has expanded considerably. Pharmaceutical researchers can now choose from technologies that, in addition to traditional high-throughput knowledge-based and diversity screening, now include the screening of fragment and fragment-like libraries, affinity selection mass spectrometry, and selection against DNA-encoded libraries (DELs). Each of these techniques has its own unique combination of advantages and limitations that makes them more, or less, suitable for different target classes or discovery objectives, such as desired mechanism of action. Layered on top of this are the constraints of the drug-hunters themselves, including budgets, timelines, and available platform capacity; each of these can play a part in dictating the hit identification strategy for a discovery program. In this article, we discuss some of the factors that we use to govern our building of a hit identification roadmap for a program and describe the increasing role that DELs are playing in our discovery strategy. Furthermore, we share our learning during our initial exploration of DEL and highlight the approaches we have evolved to maximize the value returned from DEL selections. Topics addressed include the optimization of library design and production, reagent validation, data analysis, and hit confirmation. We describe how our thinking in these areas has led us to build a DEL platform that has begun to deliver tractable matter to our global discovery portfolio.

DNA-encoded libraries (DELs): a review of on-DNA chemistries and their output

A DNA-encoded library is a collection of small molecules covalently linked to DNA that has unique information about the identity and the structure of each library member. A DNA-encoded chemical library (DEL) is broadly adopted by major pharmaceutical companies and used in numerous drug discovery programs. The application of the DEL technology is advantageous at the initial period of drug discovery because of reduced cost, time, and storage space for the identification of target compounds. The key points for the construction of DELs comprise the development and the selection of the encoding methods, transfer of routine chemical reaction from off-DNA to on-DNA, and exploration of new chemical reactions on DNA. The limitations in the chemical space and the diversity of DEL were reduced gradually by using novel DNA-compatible reactions based on the formation and the cleavage of various bonds. Here, we summarized a series of novel DNA-compatible chemistry reactions for DEL building blocks and analysed the druggability of screened hit molecules via DELs in the past five years.

In silico analysis of Phyllanthus amarus phytochemicals as potent drugs against SARS-CoV-2 main protease

Phyllanthus amarus, also known as Bhui Korma in India, is well known for its medicinal properties and is used to treat several diseases worldwide. This study aims to identify phytochemicals from P. amarus and assess their anti-viral activity through in silico methods against the main protease (3CL^Pro/M^Pro) enzyme of the novel coronavirus. 190 compounds were obtained from literature and docked against 3CLPro and 16 compounds showed higher binding affinity with 3CL^Pro with their values lying between -8.9 ​kcal/mol to -9.6 ​kcal/mol. The top two compounds, Myricitrin (CID: 5352000) and Quercetin-3-O-glucuronide (CID: 12004528) gave high binding affinity values of -9.6 ​kcal/mol and -9.4 ​kcal/mol respectively and also display favourable binding interactions with the 3CL^Pro. Both the compounds were further subjected to molecular dynamics simulation and MM-PBSA based binding free energy calculations. ADMET and drug-likeness properties were studied to assess the pharmacokinetic properties of the compounds. Favourable pharmacokinetic results reinforced the applicability of the compounds assessed. Along with continuous studies being carried out with chemical compounds, research needs to expand into all areas, including the use of natural compounds as drug compounds. The identified hits from this study can be taken further for in vitro and in vivo studies to examine their efficacy against COVID-19.

Multi-objective optimization methods in novel drug design

Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.

Discovery of EST73502, a Dual μ-Opioid Receptor Agonist and σ1 Receptor Antagonist Clinical Candidate for the Treatment of Pain

The synthesis and pharmacological activity of a new series of 4-alkyl-1-oxa-4,9-diazaspiro[5.5]undecane derivatives as potent dual ligands for the σ1 receptor (σ1R) and the μ-opioid receptor (MOR) are reported. A lead optimization program over the initial 4-aryl analogues provided 4-alkyl derivatives with the desired functionality and good selectivity and ADME profiles. Compound 14u (EST73502) showed MOR agonism and σ1R antagonism and a potent analgesic activity, comparable to the MOR agonist oxycodone in animal models of acute and chronic pain after single and repeated administration. Contrary to oxycodone, 14u produces analgesic activity with reduced opioid-induced relevant adverse events, like intestinal transit inhibition and naloxone-precipitated behavioral signs of opiate withdrawal. These results provide evidence that dual MOR agonism and σ1R antagonism may be a useful strategy for obtaining potent and safer analgesics and were the basis for the selection of 14u as a clinical candidate for the treatment of pain.

Unnarmicin D, an Anti-inflammatory Cyanobacterial Metabolite with δ and μ Opioid Binding Activity Discovered via a Pipeline Approach Designed to Target Neurotherapeutics

To combat the bottlenecks in drug discovery and development, a pipeline to identify neuropharmacological candidates using in silico, in vitro, and receptor specific assays was devised. The focus of this pipeline was to identify metabolites with the ability to reduce neuroinflammation, due to the implications that chronic neuroinflammation has in chronic pain and neurodegenerative diseases. A library of pure compounds isolated from the cyanobacterium Trichodesmium thiebautii was evaluated using this method. In silico analysis of drug likelihood and in vitro permeability analysis using the parallel artificial membrane permeability assay (PAMPA) highlighted multiple metabolites of interest from the library. Murine BV-2 microglia were used in conjunction with the Griess assay to determine if metabolites could reduce lipopolysaccharide induced neuroinflammation followed by analysis of pro-inflammatory cytokine concentrations in the supernatant of the treated cell cultures. The nontoxic metabolite unnarmicin D was further evaluated due to its moderate permeability in the PAMPA assay, promising ADME data, modulation of all cytokines tested, and prediction as an opioid receptor ligand. Molecular modeling of unnarmicin D to the μ and δ opioid receptors showed strong theoretical binding potential to the μ opioid receptor. In vitro binding assays validated this pipeline showing low micromolar binding affinity for the μ opioid receptor launching the potential for further analysis of unnarmicin D derivatives for the treatment of pain and neuroinflammation related diseases.

Structure-activity relationships for a series of (Bis(4-fluorophenyl)methyl)sulfinylethyl-aminopiperidines and -piperidine amines at the dopamine transporter: Bioisosteric replacement of the piperazine improves metabolic stability

Despite considerable efforts to develop medications to treat psychostimulant use disorders, none have proven effective, leaving an underserved patient population and unanswered questions as to what mechanism(s) of action should be targeted for developing pharmacotherapies. Atypical dopamine transporter (DAT) inhibitors, based on (±)modafinil, have shown therapeutic potential in preclinical models of psychostimulant abuse. However, metabolic instability among other limitations to piperazine analogues 1-3 have impeded further development. Herein, bioisosteric substitutions of the piperazine ring were explored with a series of aminopiperidines (A) and piperidine amines (B) wherein compounds with either a terminal tertiary amine or amide were synthesized. Several lead compounds showed high to moderate DAT affinities and metabolic stability in rat liver microsomes. Aminopiperidines 7 (DAT Ki = 50.6 nM), 21b (DAT Ki = 77.2 nM) and 33 (DAT Ki = 30.0 nM) produced only minimal stimulation of ambulatory activity in mice, compared to cocaine, suggesting an atypical DAT inhibitor profile.

EST64454: a Highly Soluble σ1 Receptor Antagonist Clinical Candidate for Pain Management

The synthesis and pharmacological activity of a new series of pyrazoles that led to the identification of 1-(4-(2-((1-(3,4-difluorophenyl)-1H-pyrazol-3-yl)methoxy)ethyl)piperazin-1-yl)ethanone (9k, EST64454) as a σ1 receptor (σ1R) antagonist clinical candidate for the treatment of pain are reported. The compound 9k is easily obtained through a five-step synthesis suitable for the production scale and shows an outstanding aqueous solubility, which together with its high permeability in Caco-2 cells will allow its classification as a BCS class I compound. It also shows high metabolic stability in all species, linked to an adequate pharmacokinetic profile in rodents, and antinociceptive properties in the capsaicin and partial sciatic nerve ligation models in mice.

2-Pyridone natural products as inhibitors of SARS-CoV-2 main protease

The disease, COVID-19, is caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2) for which there is currently no treatment. The SARS-CoV-2 main protease (M^pro) is an important enzyme for viral replication. Small molecules that inhibit this protease could lead to an effective COVID-19 treatment. The 2-pyridone scaffold was previously identified as a possible key pharmacophore to inhibit SARS-CoV-2 M^pro. A search for natural, antimicrobial products with the 2-pyridone moiety was undertaken herein, and their calculated potency as inhibitors of SARS-CoV-2 M^pro was investigated. Thirty-three natural products containing the 2-pyridone scaffold were identified from the literature. An in silico methodology using AutoDock was employed to predict the binding energies and inhibition constants (K_i values) for each 2-pyridone-containing compound with SARS-CoV-2 M^pro. This consisted of molecular optimization of the 2-pyridone compound, docking of the compound with a crystal structure of SARS-CoV-2 M^pro, and evaluation of the predicted interactions and ligand-enzyme conformations. All compounds investigated bound to the active site of SARS-CoV-2 M^pro, close to the catalytic dyad (His-41 and Cys-145). Thirteen molecules had predicted K_i values <1 μM. Glu-166 formed a key hydrogen bond in the majority of the predicted complexes, while Met-165 had some involvement in the complex binding as a close contact to the ligand. Prominent 2-pyridone compounds were further evaluated for their ADMET properties. This work has identified 2-pyridone natural products with calculated potent inhibitory activity against SARS-CoV-2 M^pro and with desirable drug-like properties, which may lead to the rapid discovery of a treatment for COVID-19.

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2-pyridone-scaffold is an inhibitory pharmacophore for SARS-CoV-2 M^pro.

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Thirty-three natural, antimicrobial products identified with 2-pyridone moiety.

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All 2-pyridone natural products bind to active site of SARS-CoV-2 M^proin silico.

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Thirteen molecules found to have potent inhibitory activity against SARS-CoV-2 M^pro.

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Inhibition of SARS-CoV-2 by natural 2-pyridones may lead to treatment of COVID-19.

Waixenicin A, a marine-derived TRPM7 inhibitor: a promising CNS drug lead

Ion channels are the third largest class of targets for therapeutic drugs. The pharmacology of ion channels is an important research area for identifying new treatment options for human diseases. The past decade or so has seen increasing interest in an ion channel protein belonging to the transient receptor potential (TRP) family, namely the melastatin subfamily member 7 (TRPM7), as an emerging drug target. TRPM7 is a bifunctional protein with a magnesium and calcium-conducting divalent ion channel fused with an active kinase domain. TRPM7 is ubiquitously expressed in human tissues, including the brain, and regulates various cell biology processes such as magnesium and calcium homeostasis, cell growth and proliferation, and embryonic development. TRPM7 provides a link between cellular metabolic status and intracellular calcium homeostasis in neurons due to TRPM7's unique sensitivity to fluctuating intracellular Mg·ATP levels. Thus, the protein plays a key role in ischemic and hypoxic neuronal cell death and brain injury, and is one of the key nonglutamate mechanisms in cerebral ischemia and stroke. Currently, the most potent and specific TRPM7 inhibitor is waixenicin A, a xenicane diterpenoid from the Hawaiian soft coral Sarcothelia edmondsoni. Using waixenicin A as a pharmacological tool, we demonstrated that TRPM7 is involved in promoting neurite outgrowth in vitro. Most recently, we found that waixenicin A reduced hypoxic-ischemic brain injury and preserved long-term behavioral outcomes in mouse neonates. We here suggest that TRPM7 is an emerging drug target for CNS diseases and disorders, and waixenicin A is a viable drug lead for these disorders.

Drug Repurposing in Medulloblastoma: Challenges and Recommendations

OPINION STATEMENT

Medulloblastoma is the most frequently diagnosed primary malignant brain tumor among children. Currently available therapeutic strategies are based on surgical resection, chemotherapy, and/or radiotherapy. However, majority of patients quickly develop therapeutic resistance and are often left with long-term therapy-related side effects and sequelae. Therefore, there remains a dire need to develop more effective therapeutics to overcome the acquired resistance to currently available therapies. Unfortunately, the process of developing novel anti-neoplastic drugs from bench to bedside is highly time-consuming and very expensive. A wide range of drugs that are already in clinical use for treating non-cancerous diseases might commonly target tumor-associated signaling pathways as well and hence be of interest in treating different cancers. This is referred to as drug repurposing or repositioning. In medulloblastoma, drug repurposing has recently gained a remarkable interest as an alternative therapy to overcome therapy resistance, wherein existing non-tumor drugs are being tested for their potential anti-neoplastic effects outside the scope of their original use.

Selective Inhibitors of G2019S-LRRK2 Kinase Activity

Pathogenic variants in the leucine-rich repeat kinase 2 (LRRK2) gene have been identified that increase the risk for developing Parkinson's disease in a dominantly inherited fashion. These pathogenic variants, of which G2019S is the most common, cause abnormally high kinase activity, and compounds that inhibit this activity are being pursued as potentially disease-modifying therapeutics. Because LRRK2 regulates important cellular processes, developing inhibitors that can selectively target the pathogenic variant while sparing normal LRRK2 activity could offer potential advantages in heterozygous carriers. We conducted a high-throughput screen and identified a single selective compound that preferentially inhibited G2019S-LRRK2. Optimization of this scaffold led to a series of novel, potent, and highly selective G2019S-LRRK2 inhibitors.

Synthesis of functionalized derivatives of the gamma-secretase modulator BMS-932481 and identification of its major metabolite

In an effort to improve physical properties by introducing polar functionality into the bicyclic pyrimidine gamma-secretase modulator (GSM) clinical candidate BMS-932481, we prepared several oxidative products of BMS-932481. Among the analogs that were prepared, the C-5 alcohol 3 was identified as the predominant metabolite of BMS-932481 found in rat and human liver microsomes. Alcohol 3 was determined to be chemically unstable, leading to the hypothesis that 3 may lead to the production of reactive species both in vitro and in vivo.

5-Phenyl-1,3,4-oxadiazol-2(3H)-ones Are Potent Inhibitors of Notum Carboxylesterase Activity Identified by the Optimization of a Crystallographic Fragment Screening Hit

Carboxylesterase Notum is a negative regulator of the Wnt signaling pathway. There is an emerging understanding of the role Notum plays in disease, supporting the need to discover new small-molecule inhibitors. A crystallographic X-ray fragment screen was performed, which identified fragment hit 1,2,3-triazole 7 as an attractive starting point for a structure-based drug design hit-to-lead program. Optimization of 7 identified oxadiazol-2-one 23dd as a preferred example with properties consistent with drug-like chemical space. Screening 23dd in a cell-based TCF/LEF reporter gene assay restored the activation of Wnt signaling in the presence of Notum. Mouse pharmacokinetic studies with oral administration of 23dd demonstrated good plasma exposure and partial blood-brain barrier penetration. Significant progress was made in developing fragment hit 7 into lead 23dd (>600-fold increase in activity), making it suitable as a new chemical tool for exploring the role of Notum-mediated regulation of Wnt signaling.

Feasibility studies of radioiodinated pyridyl benzofuran derivatives as potential SPECT imaging agents for prion deposits in the brain

INTRODUCTION

Prion diseases are fatal neurodegenerative disorders caused by the deposition of abnormal prion protein aggregates (PrPSc) in the central nervous system. This study aimed to evaluate the use of iodinated pyridyl benzofuran (IPBF) derivatives as single-photon emission computed tomography (SPECT) probes for the detection of cerebral PrPSc deposits.

METHODS

In vitro binding assays of IPBF derivatives were carried out in the recombinant mouse prion protein (rMoPrP) and brain sections of mouse-adapted bovine spongiform encephalopathy (mBSE)-infected mice. SPECT imaging of 5-(5-[123I]iodobenzofuran-2-yl)-N-methylpyridin-2-amine ([123I]IPBF-NHMe) was performed on mBSE-infected and mock-infected mice.

RESULTS

Fluorescence microscopy results showed that fluorescence signals of IPBF derivatives corresponded to the thioflavin-T positive amyloid deposits of PrPSc in the brain sections of mouse-adapted bovine spongiform encephalopathy (mBSE)-infected mice. Among the IPBF derivatives, 5-(5-iodobenzofuran-2-yl)-N-methylpyridin-2-amine (IPBF-NHMe) exhibited the highest binding affinity to the recombinant mouse prion protein (rMoPrP) aggregates with a Ki of 14.3 nM. SPECT/computed tomography (CT) imaging and ex vivo autoradiography demonstrated that the [123I]IPBF-NHMe distribution in brain tissues of mBSE-infected mice co-localized with PrPSc deposits.

CONCLUSION

[123I]IPBF-NHMe appears to be a prospective SPECT tracer for monitoring prion deposits in living brain tissues.

REINVENT 2.0: An AI Tool for De Novo Drug Design

In the past few years, we have witnessed a renaissance of the field of molecular de novo drug design. The advancements in deep learning and artificial intelligence (AI) have triggered an avalanche of ideas on how to translate such techniques to a variety of domains including the field of drug design. A range of architectures have been devised to find the optimal way of generating chemical compounds by using either graph- or string (SMILES)-based representations. With this application note, we aim to offer the community a production-ready tool for de novo design, called REINVENT. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. It can facilitate the idea generation process by bringing to the researcher's attention the most promising compounds. REINVENT's code is publicly available at https://github.com/MolecularAI/Reinvent.

Design of small molecules targeting RNA structure from sequence

The design and discovery of small molecule medicines has largely been focused on a small number of druggable protein families. A new paradigm is emerging, however, in which small molecules exert a biological effect by interacting with RNA, both to study human disease biology and provide lead therapeutic modalities. Due to this potential for expanding target pipelines and treating a larger number of human diseases, robust platforms for the rational design and optimization of small molecules interacting with RNAs (SMIRNAs) are in high demand. This review highlights three major pillars in this area. First, the transcriptome-wide identification and validation of structured RNA elements, or motifs, within disease-causing RNAs directly from sequence is presented. Second, we provide an overview of high-throughput screening approaches to identify SMIRNAs as well as discuss the lead identification strategy, Inforna, which decodes the three-dimensional (3D) conformation of RNA motifs with small molecule binding partners, directly from sequence. An emphasis is placed on target validation methods to study the causality between modulating the RNA motif in vitro and the phenotypic outcome in cells. Third, emergent modalities that convert occupancy-driven mode of action SMIRNAs into event-driven small molecule chemical probes, such as RNA cleavers and degraders, are presented. Finally, the future of the small molecule RNA therapeutics field is discussed, as well as hurdles to overcome to develop potent and selective RNA-centric chemical probes.

Inhibition of TRPM7 with waixenicin A reduces glioblastoma cellular functions

Glioblastoma (GBM) is the most common malignant primary brain tumour originating in the CNS. Median patient survival is <15 months with standard treatment which consists of surgery alongside radiation therapy and temozolomide chemotherapy. However, because of the aggressive nature of GBM, and the significant toxicity of these adjuvant therapies, long-term therapeutic effects are unsatisfactory. Thus, there is urgency to identify new drug targets for GBM. Recent evidence shows that the transient receptor potential melastatin 7 (TRPM7) cation channel is aberrantly upregulated in GBM and its inhibition leads to reduction of GBM cellular functions. This suggests that TRPM7 may be a potential drug target for GBM treatment. In this study, we assessed the effects of the specific TRPM7 antagonist waixenicin A on human GBM cell lines U87 or U251 both in vitro and in vivo. First, we demonstrated in vitro that application of waixenicin A reduced TRPM7 protein expression and inhibited the TRPM7-like currents in GBM cells. We also observed reduction of GBM cell viability, migration, and invasion. Using an intracranial xenograft GBM mouse model, we showed that with treatment of waixenicin A, there was increased cleaved caspase 3 activity, alongside reduction in Ki-67, cofilin, and Akt activity in vivo. Together, these data demonstrate higher GBM cell apoptosis, and lower proliferation, migration, invasion and survivability following treatment with waixenicin A.

Selective inhibition of the K+ efflux sensitive NLRP3 pathway by Cl- channel modulation

The NLRP3 inflammasome regulates production of the pro-inflammatory cytokines interleukin-1β (IL-1β) and IL-18, and contributes to inflammation exacerbating disease. Fenamate non-steroidal anti-inflammatory drugs (NSAIDs) were recently described as NLRP3 inflammasome inhibitors via chloride channel inhibition. Fenamate NSAIDs inhibit cyclooxygenase (COX) enzymes, limiting their potential as therapeutics for NLRP3-associated diseases due to established side effects. The aim here was to develop properties of the fenamates that inhibit NLRP3, and at the same time to reduce COX inhibition. We synthesised a library of analogues, with feedback from in silico COX docking potential, and IL-1β release inhibitory activity. Through iterative screening and rational chemical design, we established a collection of chloride channel inhibiting active lead molecules with potent activity at the canonical NLRP3 inflammasome and no activity at COX enzymes, but only in response to stimuli that activated NLRP3 by a K⁺ efflux-dependent mechanism. This study identifies a model for the isolation and removal of unwanted off-target effects, with the enhancement of desired activity, and establishes a new chemical motif for the further development of NLRP3 inflammasome inhibitors.

The NLRP3 inflammasome regulates production of the pro-inflammatory cytokines interleukin-1β (IL-1β) and IL-18, and contributes to inflammation exacerbating disease.

Synthesis of 17β-hydroxysteroid dehydrogenase type 10 steroidal inhibitors: Selectivity, metabolic stability and enhanced potency

17beta-Hydroxysteroid dehydrogenase type 10 (17β-HSD10) is the only mitochondrial member of 17β-HSD family. This enzyme can oxidize estradiol (E2) into estrone (E1), thus reducing concentration of this neuroprotective steroid. Since 17β-HSD10 possesses properties that suggest a possible role in Alzheimer's disease, its inhibition appears to be a therapeutic strategy. After we identified the androsterone (ADT) derivative 1 as a first steroidal inhibitor of 17β-HSD10, new analogs were synthesized to increase the metabolic stability, to improve the selectivity of inhibition over 17β-HSD3 and to optimize the inhibitory potency. From six D-ring derivatives of 1 (17-CO), two compounds (17β-H/17α-OH and 17β-OH/17α-CCH) were more metabolically stable and did not inhibit the 17β-HSD3. Moreover, solid phase synthesis was used to extend the molecular diversity on the 3β-piperazinylmethyl group of the steroid base core. Eight over 120 new derivatives were more potent inhibitors than 1 for the transformation of E2 to E1, with the 4-(4-trifluoromethyl-3-methoxybenzyl)piperazin-1-ylmethyl-ADT (D-3,7) being 16 times more potent (IC₅₀ = 0.14 μM). Finally, D-ring modification of D-3,7 provided 17β-OH/17α-CCH derivative 25 and 17β-H/17α-OH derivative 26, which were more potent inhibitor than 1 (1.8 and 2.4 times, respectively).

The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization

Matched Molecular Pairs (MMP) analysis is a well-established technique for Structure Activity and Property Analysis (SAR and SPR). Summarizing multiple MMPs that describe the same structural change into a single chemical transform can be a powerful tool for prediction (termed Transform from here on). This is particularly useful in the area of Absorption, Distribution, Metabolism, and Elimination (ADME) analysis that is less influenced by 3D structural binding effects. The creation of a knowledge database containing many of these Transforms across typical ADME assays promises to be a powerful approach to aid multidimensional optimization. We present a detailed workflow for the derivation of such a database. We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. This is made freely available as part of the LillyMol software package. We describe the application of this method to several ADME/Tox (Toxicity) assay data sets and highlight multiple cases where the impact of traditional medicinal chemistry Transforms is contradicted by MMP data. We also describe the internal software interface used by medicinal chemists to aid the design of new compounds via automated suggestion. This approach utilizes the matched pairs database to "suggest" improved compounds in an automated design scenario. A nonvisual script-based version of the automated suggestions code with an associated set of described chemical Transforms is also made freely available along with this paper and as part of the LillyMol software package. Finally, we contrast this knowledge database against a larger database of all MMPs derived from a 2 million compound diversity set and a subset of MMPs seen in historical discovery projects. The comparison against all transforms in the diversity collection highlights the very low coverage of the transform database as compared to all possible transforms involving 15 atom fragments. The comparison against a smaller subset of Transforms seen on internal Medicinal Chemistry projects shows better coverage of the transform database for a small set of common medicinal chemistry strategies. Within the context of all possible transforms available to a medicinal chemistry project team, the challenge remains to move beyond mere idea generation from past projects toward high quality prediction for novel ADME/Tox modulating Transforms.