Probabilistic Approach to Generating MPOs and Its Application as a Scoring Function for CNS Drugs

Present Scenario and Future Landscape of Payloads for ADCs: Focus on DNA-Interacting Agents

ADCs have emerged as a promising class of therapeutics, combining the targeting specificity of monoclonal antibodies with the cytotoxic potency of small-molecule drugs. Although the majority of approved ADCs are still based on microtubule binder payloads, the recent success of topoisomerase I inhibitors has revitalized interest in the identification of novel agents overcoming present limitations in the field including narrow therapeutic window and chemoresistance. The success of DNA binders as payload for ADCs has been very limited, up to now, due, among other factors, to high hydrophobicity and planar chemical structures resulting in most cases in ADCs with a strong tendency to aggregate, poor plasma stability, and limited therapeutic index. Some of these molecules, however, continue to be of interest due to their favorable properties in terms of cytotoxic potency even in chemoresistant settings, bystander and immunogenic cell death effects, and known combinability with approved drugs. We critically evaluated several clinically tested ADCs containing DNA binders, focusing on payload physicochemical properties, cytotoxic potency, and obtained clinical results. Our analysis suggests that further exploration of certain chemical classes, specifically anthracyclines and duocarmycins, based on the optimization of physicochemical parameters, reduction of cytotoxic potency, and careful design of targeting molecules is warranted. This approach will possibly result in a novel generation of payloads overcoming the limitations of clinically validated ADCs.

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

Discovery and Optimization of N-Heteroaryl Indazole LRRK2 Inhibitors

Inhibition of leucine-rich repeat kinase 2 is a genetically supported mechanism for the treatment of Parkinson's disease. We previously disclosed the discovery of an indazole series lead that demonstrated both safety and translational risks. The safety risks were hypothesized to be of unknown origin, so structural diversity in subsequent chemical matter was prioritized. The translational risks were identified due to a low brain Kpu,u in nonhuman primate studies, which raised concern over the use of an established peripheral biomarker as a surrogate for central target engagement. Given these challenges, the team sought to leverage structure- and property-based drug design and expanded efflux transporter profiling to identify structurally distinct leads with enhanced CNS drug-likeness. Herein, we describe the discovery of a "reinvented" indazole series with improved physicochemical properties and efflux transporter profiles while maintaining excellent potency and off-target kinase selectivity, which resulted in advanced lead, compound 23.

CNS delivery of targeted protein degraders

Heterobifunctional small molecule degraders are a subset of targeted protein degraders (TPDs), consisting of two ligands joined by a linker to induce proteasomal degradation of a target protein. As compared to traditional small molecules these compounds generally demonstrate inflated physicochemical properties, which may require innovative formulation strategies to enable their delivery and exert pharmacodynamic effect. The blood brain barrier (BBB) serves an essential function in human physiology, but its presence requires advanced approaches for treating central nervous system (CNS) diseases. By integrating emerging modalities like TPDs with conventional concepts of drug delivery, novel strategies to overcome the BBB can be developed. Amongst the available routes, lipid and polymer-based long-acting delivery seems to be the most amenable to TPDs, due to their ability to encapsulate lipophilic cargo and potential to be functionalized for targeted delivery. Another key consideration will be understanding E3 ligase expression in the different regions of the brain. Discovery of new brain or CNS disease specific E3 ligases could help overcome some of the barriers currently associated with CNS delivery of TPDs. This review discusses the current strategies that exist to overcome and improve therapeutic delivery of TPDs to the CNS.

Novel 4-Aryl-4H-chromene derivative displayed excellent in vivo anti-glioblastoma efficacy as the microtubule-targeting agent

In this study, a series of novel 4-Aryl-4H-chromene derivatives (D1-D31) were designed and synthesized by integrating quinoline heterocycle to crolibulin template molecule based on the strategy of molecular hybridization. One of these compounds D19 displayed positive antiproliferative activity against U87 cancer cell line (IC50 = 0.90 ± 0.03 μM). Compound D19 was verified as the microtubule-targeting agent through downregulating tubulin related genes of U87 cells, destroying the cytoskeleton of tubulins and interacting with the colchicine-binding site to inhibit the polymerization of tubulins by transcriptome analysis, immune-fluorescence staining, microtubule dynamics and EBI competition assays as well as molecular docking simulations. Moreover, compound D19 induced G2/M phase arrest, resulted in cell apoptosis and inhibited the migration of U87 cells by flow cytometry analysis and wound healing assays. Significantly, compound D19 dose-dependently inhibited the tumor growth of orthotopic glioma xenografts model (GL261-Luc) and effectively prolonged the survival time of mice, which were extremely better than those of positive drug temozolomide (TMZ). Compound D19 exhibited potent in vivo antivascular activity as well as no observable toxicity. Furthermore, the results of in silico simulation studies and P-gp transwell assays verified the positive correlation between compound D19's Blood-Brain Barrier (BBB) permeability and its in vivo anti-GBM activity. Overall, compound D19 can be used as a promising anti-GBM lead compound for the treatment of glioblastoma.

Synthesis and biological evaluation of novel 18F-labeled 2,4-diaminopyrimidine derivatives for detection of ghrelin receptor in the brain

The ghrelin receptor (GHSR) is known to regulate various physiological processes including appetite, food intake, and growth hormone release. Its expression is mainly observed in the brain, pancreas, stomach, and intestine. However, the functions of the receptor have not been fully elucidated. GHSR imaging with positron emission tomography (PET) is expected to further understanding of the functions and pathologies of the receptor. In this study, we newly designed and synthesized diaminopyrimidine derivatives ([18F]BPP-1 and [18F]BPP-2) and evaluated their utility as novel PET probes targeting GHSR. In in vitro competitive binding assays, the binding affinity of BPP-2 for GHSR (Ki = 274 nM) was comparable to that of the diaminopyimidine lead compound Abb8a (Ki = 109 nM). In a biodistribution study using normal mice, [18F]BPP-2 displayed low uptake in the brain and moderate uptake in the pancreas, but high radioactivity accumulation in bone was observed due to its defluorination in vivo. Taken together, although further improvement of the pharmacokinetics is needed, the diaminopyrimidine scaffold has potential for the development of useful GHSR-targeting PET probes.

Trends in Neosubstrate Degradation by Cereblon-Based Molecular Glues and the Development of Novel Multiparameter Optimization Scores

Molecular glues enable the degradation of previously "undruggable" proteins via the recruitment of cereblon (CRBN) to the target. One major challenge in designing CRBN E3 ligase modulating compounds (CELMoDs) is the selectivity profile toward neosubstrates, proteins recruited by CRBN E3 ligase agents for degradation. Common neosubstrates include Aiolos, Ikaros, GSPT1, CK1α, and SALL4. Unlike achieving potency and selectivity for traditional small molecule inhibitors, reducing the degradation of these neosubstrates is complicated by the ternary nature of the complex formed between the protein, CRBN, and CELMoD. The standard guiding principles of medicinal chemistry, such as enforcing hydrogen bond formation, are less predictive of degradation efficiency and selectivity. Disclosed is an analysis of our glutarimide CELMoD library to identify interpretable chemical features correlated to selectivity profiles and general cytotoxicity. Included is a simple multiparameter optimization function using only three parameters to predict whether molecules will have undesired neosubstrate activity.

Discovery of MK-1468: A Potent, Kinome-Selective, Brain-Penetrant Amidoisoquinoline LRRK2 Inhibitor for the Potential Treatment of Parkinson's Disease

Genetic mutation of the leucine-rich repeat kinase 2 (LRRK2) protein has been associated with Parkinson's disease (PD), a disabling and progressive neurodegenerative disorder that is devoid of efficacious disease-modifying therapies. Herein, we describe the invention of an amidoisoquinoline (IQ)-derived LRRK2 inhibitor lead chemical series. Knowledge-, structure-, and property-based drug design in concert with rigorous application of in silico calculations and presynthesis predictions enabled the prioritization of molecules with favorable CNS "drug-like" physicochemical properties. This resulted in the discovery of compound 8, which was profiled extensively before human ether-a-go-go (hERG) ion channel inhibition halted its progression. Strategic reduction of lipophilicity and basicity resulted in attenuation of hERG ion channel inhibition while maintaining a favorable CNS efflux transporter profile. Further structure- and property-based optimizations resulted in the discovery of preclinical candidate MK-1468. This exquisitely selective LRRK2 inhibitor has a projected human dose of 48 mg BID and a preclinical safety profile that supported advancement toward GLP toxicology studies.

A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, Kp,uu

The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (K_p,uu). While various medicinal chemistry strategies and in silico models have been reported to improve BBB penetration, they have limited application in predicting K_p,uu directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict K_p,uu. Prospective application of this method in internal CNS drug discovery programs highlights the utility and accuracy of this new method, which showed a categorical accuracy of 79% and an R² of 0.61 from a linear regression model.

Recent advances in dopamine D2 receptor ligands in the treatment of neuropsychiatric disorders

Dopamine is a biologically active amine synthesized in the central and peripheral nervous system. This biogenic monoamine acts by activating five types of dopamine receptors (D_1-5 Rs), which belong to the G protein-coupled receptor family. Antagonists and partial agonists of D₂ Rs are used to treat schizophrenia, Parkinson's disease, depression, and anxiety. The typical pharmacophore with high D₂ R affinity comprises four main areas, namely aromatic moiety, cyclic amine, central linker and aromatic/heteroaromatic lipophilic fragment. From the literature reviewed herein, we can conclude that 4-(2,3-dichlorophenyl), 4-(2-methoxyphenyl)-, 4-(benzo[b]thiophen-4-yl)-1-substituted piperazine, and 4-(6-fluorobenzo[d]isoxazol-3-yl)piperidine moieties are critical for high D₂ R affinity. Four to six atoms chains are optimal for D₂ R affinity with 4-butoxyl as the most pronounced one. The bicyclic aromatic/heteroaromatic systems are most frequently occurring as lipophilic appendages to retain high D₂ R affinity. In this review, we provide a thorough overview of the therapeutic potential of D₂ R modulators in the treatment of the aforementioned disorders. In addition, this review summarizes current knowledge about these diseases, with a focus on the dopaminergic pathway underlying these pathologies. Major attention is paid to the structure, function, and pharmacology of novel D₂ R ligands, which have been developed in the last decade (2010-2021), and belong to the 1,4-disubstituted aromatic cyclic amine group. Due to the abundance of data, allosteric D₂ R ligands and D₂ R modulators from patents are not discussed in this review.

Discovery and Optimization of Potent, Selective, and Brain-Penetrant 1-Heteroaryl-1H-Indazole LRRK2 Kinase Inhibitors for the Treatment of Parkinson's Disease

Inhibition of leucine-rich repeat kinase 2 (LRRK2) kinase activity represents a genetically supported, chemically tractable, and potentially disease-modifying mechanism to treat Parkinson's disease. Herein, we describe the optimization of a novel series of potent, selective, central nervous system (CNS)-penetrant 1-heteroaryl-1H-indazole type I (ATP competitive) LRRK2 inhibitors. Type I ATP-competitive kinase physicochemical properties were integrated with CNS drug-like properties through a combination of structure-based drug design and parallel medicinal chemistry enabled by sp³-sp² cross-coupling technologies. This resulted in the discovery of a unique sp³-rich spirocarbonitrile motif that imparted extraordinary potency, pharmacokinetics, and favorable CNS drug-like properties. The lead compound, 25, demonstrated exceptional on-target potency in human peripheral blood mononuclear cells, excellent off-target kinase selectivity, and good brain exposure in rat, culminating in a low projected human dose and a pre-clinical safety profile that warranted advancement toward pre-clinical candidate enabling studies.

Target-Agnostic P-Glycoprotein Assessment Yields Strategies to Evade Efflux, Leading to a BRAF Inhibitor with Intracranial Efficacy

The blood-brain barrier (BBB) presents a major hurdle in the development of central nervous system (CNS) active therapeutics, and expression of the P-glycoprotein (P-gp) efflux transporter at the blood-brain interface further impedes BBB penetrance of most small molecules. Designing efflux liabilities out of compounds can be laborious, and there is currently no generalizable approach to directly transform periphery-limited agents to ones active in the CNS. Here, we describe a target-agnostic, prospective assessment of P-gp efflux using diverse compounds. Our results demonstrate that reducing the molecular size or appending a carboxylic acid in many cases enables evasion of P-gp efflux in cell-based experiments and in mice. These strategies were then applied to transform a periphery-limited ^V600EBRAF inhibitor, dabrafenib, into versions that possess potent and selective anti-cancer activity but now also evade P-gp-mediated efflux. When compared to dabrafenib, the compound developed herein (everafenib) has superior BBB penetrance and superior efficacy in an intracranial mouse model of metastatic melanoma, suggesting it as a lead candidate for the treatment of melanoma metastases to the brain and gliomas with BRAF mutation. More generally, the results described herein suggest the actionability of the trends observed in these target-agnostic efflux studies and provide guidance for the conversion of non-BBB-penetrant drugs into versions that are BBB-penetrant and efficacious.

In Silico Approaches for Addressing Challenges in CNS Radiopharmaceutical Design

Positron emission tomography (PET) is a highly sensitive and versatile molecular imaging modality that leverages radiolabeled molecules, known as radiotracers, to interrogate biochemical processes such as metabolism, enzymatic activity, and receptor expression. The ability to probe specific molecular and cellular events longitudinally in a noninvasive manner makes PET imaging a particularly powerful technique for studying the central nervous system (CNS) in both health and disease. Unfortunately, developing and translating a single CNS PET tracer for clinical use is typically an extremely resource-intensive endeavor, often requiring synthesis and evaluation of numerous candidate molecules. While existing in vitro methods are beginning to address the challenge of derisking molecules prior to costly in vivo PET studies, most require a significant investment of resources and possess substantial limitations. In the context of CNS drug development, significant time and resources have been invested into the development and optimization of computational methods, particularly involving machine learning, to streamline the design of better CNS therapeutics. However, analogous efforts developed and validated for CNS radiotracer design are conspicuously limited. In this Perspective, we overview the requirements and challenges of CNS PET tracer design, survey the most promising computational methods for in silico CNS drug design, and bridge these two areas by discussing the potential applications and impact of computational design tools in CNS radiotracer design.

Defining Levels of Automated Chemical Design

One application area of computational methods in drug discovery is the automated design of small molecules. Despite the large number of publications describing methods and their application in both retrospective and prospective studies, there is a lack of agreement on terminology and key attributes to distinguish these various systems. We introduce Automated Chemical Design (ACD) Levels to clearly define the level of autonomy along the axes of ideation and decision making. To fully illustrate this framework, we provide literature exemplars and place some notable methods and applications into the levels. The ACD framework provides a common language for describing automated small molecule design systems and enables medicinal chemists to better understand and evaluate such systems.

Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schrödinger Computational Platform

d-Serine is a coagonist of the N-methyl d-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, d-serine is synthesized from its l-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO). Many studies have linked decreased d-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, it is feasible to employ DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with the best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound potency. A new class of DAO inhibitors with desirable properties has been discovered from this endeavor. Our modeling technology on this program has not only enhanced the efficiency of structure-activity relationship development but also helped to identify a previously unexplored subpocket for further optimization.

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

The Application of in silico Methods for Prediction of Blood-Brain Barrier Permeability of Small Molecule PET Tracers

Designing positron emission tomography (PET) tracers for targets in the central nervous system (CNS) is challenging. Besides showing high affinity and high selectivity for their intended target, these tracers have to be able to cross the blood-brain barrier (BBB). Since only a small fraction of small molecules is estimated to be able to cross the BBB, tools that can predict permeability at an early stage during the development are of great importance. One such tool is in silico models for predicting BBB-permeability. Thus far, such models have been built based on CNS drugs, with one exception. Herein, we sought to discuss and analyze if in silico predictions that have been built based on CNS drugs can be applied for CNS PET tracers as well, or if dedicated models are needed for the latter. Depending on what is taken into account in the prediction, i.e., passive diffusion or also active influx/efflux, there may be a need for a model build on CNS PET tracers. Following a brief introduction, an overview of a few selected in silico BBB-permeability predictions is provided along with a short historical background to the topic. In addition, a combination of previously reported CNS PET tracer datasets were assessed in a couple of selected models and guidelines for predicting BBB-permeability. The selected models were either predicting only passive diffusion or also the influence of ADME (absorption, distribution, metabolism and excretion) parameters. To conclude, we discuss the potential need of a prediction model dedicated for CNS PET tracers and present the key issues in respect to setting up a such a model.

Thermodynamic properties of active pharmaceutical ingredients that are of interest in COVID-19

The pure component properties are estimated for active pharmaceutical ingredients that are related or proposed for the treatment of severe acute respiratory syndrome-CoronaVirus-2. These include Baricitinib, Camostat, Chloroquine, Dexamethasone, Hydroxychloroquine, Fingolimod, Favipiravir, Thalidomide, and Umifenovir. The estimations are based on group contribution⁺ (GC) models that contain combined group contribution and atom connectivity index with uncertainties in the estimated property values. The thermodynamic properties that are reported include boiling point, critical temperature, critical pressure, critical volume, melting point, standard Gibb's energy of formation, standard enthalpy of formation, enthalpy of fusion, enthalpy of vaporization at 298 K, enthalpy of vaporization at boiling point, entropy of vaporization at boiling point, flash point, Hildebrand solubility parameter, octanol/water partition coefficient, acentric factor, and liquid molar volume at 298 K. The reported properties are not available in the literature and thereby is an incremental development for reliable process engineering.

Structure-Guided Discovery of Aminoquinazolines as Brain-Penetrant and Selective LRRK2 Inhibitors

The leucine-rich repeat kinase 2 (LRRK2) protein has been genetically and functionally linked to Parkinson's disease (PD), a disabling and progressive neurodegenerative disorder whose current therapies are limited in scope and efficacy. In this report, we describe a rigorous hit-to-lead optimization campaign supported by structural enablement, which culminated in the discovery of brain-penetrant, candidate-quality molecules as represented by compounds 22 and 24. These compounds exhibit remarkable selectivity against the kinome and offer good oral bioavailability and low projected human doses. Furthermore, they showcase the implementation of stereochemical design elements that serve to enable a potency- and selectivity-enhancing increase in polarity and hydrogen bond donor (HBD) count while maintaining a central nervous system-friendly profile typified by low levels of transporter-mediated efflux and encouraging brain penetration in preclinical models.

Strategies for Structural Modification of Small Molecules to Improve Blood-Brain Barrier Penetration: A Recent Perspective

In the development of central nervous system (CNS) drugs, the blood-brain barrier (BBB) restricts many drugs from entering the brain to exert therapeutic effects. Although many novel delivery methods of large molecule drugs have been designed to assist transport, small molecule drugs account for the vast majority of the CNS drugs used clinically. From this perspective, we review studies from the past five years that have sought to modify small molecules to increase brain exposure. Medicinal chemists make it easier for small molecules to cross the BBB by improving diffusion, reducing efflux, and activating carrier transporters. On the basis of their excellent work, we summarize strategies for structural modification of small molecules to improve BBB penetration. These strategies are expected to provide a reference for the future development of small molecule CNS drugs.

The medicinal chemistry of mitochondrial dysfunction: a critical overview of efforts to modulate mitochondrial health

Mitochondria are subcellular organelles that perform a variety of critical biological functions, including ATP production and acting as hubs of immune and apoptotic signalling. Mitochondrial dysfunction has been extensively linked to the pathology of multiple neurodegenerative disorders, resulting in significant investment from the drug discovery community. Despite extensive efforts, there remains no disease modifying therapies for neurodegenerative disorders. This manuscript aims to review the compounds historically used to modulate the mitochondrial network through the lens of modern medicinal chemistry, and to offer a perspective on the evidence that relevant exposure was achieved in a representative model and that exposure was likely to result in target binding and engagement of pharmacology. We hope this manuscript will aid the community in identifying those targets and mechanisms which have been convincingly (in)validated with high quality chemical matter, and those for which an opportunity exists to explore in greater depth.

Modulation of biopharmaceutical properties of acidic drugs using cationic counterions: A critical analysis of FDA-approved pharmaceutical salts

Salification has a successful track record in modulating the biopharmaceutical properties of drugs. This is evident from the significant share (40%) of pharmaceutical salts in FDA-approved drugs in the past 80-years. Based on the ionic nature of drugs, the corresponding cationic or anionic counterions are employed for salification. This review aims to provide the contribution of cationic counterions in FDA-approved drugs from 1939 to 2020. The analysis of 80-years data has shown that the 7.1% of the FDA-approved drugs comprise cationic counterions (98 pharmaceutical salts). Heparin sodium is the pioneering drug in the history of pharmaceutical salts that was approved in 1939 as an anticoagulant medication. Inorganic (sodium, calcium, potassium, magnesium, silver), as well as organic (tromethamine, meglumine, erbumine) cationic counterions, were used in FDA-approved drugs with a major share by sodium (76 drugs). The technical superiority of cationic salts over other salt forms and the parent drug is also exemplified using case studies.

Carboxylic Acid Counterions in FDA-Approved Pharmaceutical Salts

Salification is one of the powerful and widely employed approaches to improve the biopharmaceutical properties of drugs. The FDA's eighty-year trajectory of new drug approvals depicts around one-third of the drugs clinically used as their pharmaceutical salts. Among various cationic and anionic counterions used in FDA-approved pharmaceutical salts, the carboxylic acids have significantly contributed. A total of 94 pharmaceutical salts discovered during 1943-2020 comprises carboxylic acids as counterions with a major contribution of acetate, maleate, tartrate, fumarate, and succinate. Hydrocodone tartrate is the first FDA-approved carboxylate salt approved in 1943. Overall, the analysis shows that fifteen carboxylic acid counterions are present in FDA-approved pharmaceutical salts with a major share of acetate (18 drugs). This review provides an account of FDA-approved carboxylate salts from 1939 to 2020. The decade-wise analysis indicates that 1991-2000 contributed a maximum number of carboxylate salts (24) and least (3) in 1939-1950. The technical advantage of carboxylate salts over free-base or other counterions is also discussed. Graphical Abstract.

Discovery of Arylsulfonamide Nav1.7 Inhibitors: IVIVC, MPO Methods, and Optimization of Selectivity Profile

The voltage-gated sodium channel Nav1.7 continues to be a high-profile target for the treatment of various pain afflictions due to its strong human genetic validation. While isoform selective molecules have been discovered and advanced into the clinic, to date, this target has yet to bear fruit in the form of marketed therapeutics for the treatment of pain. Lead optimization efforts over the past decade have focused on selectivity over Nav1.5 due to its link to cardiac side effects as well as the translation of preclinical efficacy to man. Inhibition of Nav1.6 was recently reported to yield potential respiratory side effects preclinically, and this finding necessitated a modified target selectivity profile. Herein, we report the continued optimization of a novel series of arylsulfonamide Nav1.7 inhibitors to afford improved selectivity over Nav1.6 while maintaining rodent oral bioavailability through the use of a novel multiparameter optimization (MPO) paradigm. We also report in vitro-in vivo correlations from Nav1.7 electrophysiology protocols to preclinical models of efficacy to assist in projecting clinical doses. These efforts produced inhibitors such as compound 19 with potency against Nav1.7, selectivity over Nav1.5 and Nav1.6, and efficacy in behavioral models of pain in rodents as well as inhibition of rhesus olfactory response indicative of target modulation.

Essential Medicinal Chemistry of Essential Medicines

Since 1977, the World Health Organization publishes a list of essential medicines, i.e., those that satisfy the priority health care needs of the population and are selected with regard to disease prevalence and public health relevance, evidence of clinical efficacy, and safety, as well as comparative costs and cost-effectiveness. The Essential Medicines List (EML) is an invaluable tool for all countries to select those medicines that have an excellent risk/benefit ratio and that are reputed to be of pivotal importance to health. In the present perspective, we describe the chemical composition and the main features of the small molecules that are included in the EML, spanning from their origin, to their stereochemistry and measure of drug-likeness. Most and foremost, we wish to disseminate the importance of the EML, which can be both a helpful teaching tool in an ever-expanding world of medicines and an inspiration for those involved in pharmaceutical R&D.

The Blood-Brain Barrier (BBB) Score

The blood-brain barrier (BBB) protects the brain from the toxic side effects of drugs and exogenous molecules. However, it is crucial that medications developed for neurological disorders cross into the brain in therapeutic concentrations. Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated "BBB Score", composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. On the basis of statistical analyses of our results, the BBB Score outperformed (AUC = 0.86) currently employed MPO approaches (MPO, AUC = 0.61; MPO_V2, AUC = 0.67). Initial evaluation of physicochemical property space using the BBB Score is a valuable addition to currently available drug design algorithms.

Practical approaches to evaluating and optimizing brain exposure in early drug discovery

Developing drugs for CNS related diseases continues to be one of the most challenging endeavors in drug discovery. This is at least in part related to the existence of the Blood Brain Barrier (BBB), a complex multicellular organization that provides selective access to required nutrients and hormones, while removing waste and restricting exposure to potential harmful toxins, pathogens, and xenobiotics. Consequently, designing and selecting molecules that can overcame this protection system are unique and critical aspects of the CNS drug discovery. Here we review modern CNS pharmacokinetic concepts and methods suitable for early drug discovery, and medicinal chemistry strategies towards molecules with optimal CNS exposure.

Modular synthesis of thirty lead-like scaffolds suitable for CNS drug discovery

A modular synthetic approach was developed that yielded thirty diverse lead-like scaffolds suitable for CNS drug discovery.

CNS Physicochemical Property Space Shaped by a Diverse Set of Molecules with Experimentally Determined Exposure in the Mouse Brain

Understanding the "limits and boundaries" of the central nervous system (CNS) property space is a critical aspect of modern CNS drug design. Medicinal chemists are often guided by the physicochemical properties of marketed CNS drugs, which are heavily biased toward "traditional" aminergic targets and commonly described as small lipophilic amines. This miniperspective describes the statistical analysis of the calculated physicochemical properties for a diverse set of ligands for mostly "nontraditional" CNS targets and classified as either "brain penetrant" or "peripherally restricted" on the basis of the experimental mouse brain exposure. The results suggested that (a) the physicochemical property space conducive to brain exposure is larger than the one defined by the marketed CNS drugs and (b) the most critical brain exposure determinants are descriptors of the molecular size and hydrogen bond capacity. These findings led to a modified version of the CNS MPO scoring algorithm, termed CNS MPO.v2.

Technically Extended MultiParameter Optimization (TEMPO): An Advanced Robust Scoring Scheme To Calculate Central Nervous System Druggability and Monitor Lead Optimization

At the discovery stage, it is important to understand the drug design concepts for a CNS drug compared to those for a non-CNS drug. Previously, we published on ideal CNS drug property space and defined in detail the physicochemical property distribution of CNS versus non-CNS oral drugs, the application of radar charting (a graphical representation of multiple physicochemical properties used during CNS lead optimization), and a recursive partition classification tree to differentiate between CNS- and non-CNS drugs. The objective of the present study was to further understand the differentiation of physicochemical properties between CNS and non-CNS oral drugs by the development and application of a new CNS scoring scheme: Technically Extended MultiParameter Optimization (TEMPO). In this multiparameter method, we identified eight key physicochemical properties critical for accurately assessing CNS druggability: (1) number of basic amines, (2) carbon-heteroatom (non-carbon, non-hydrogen) ratio, (3) number of aromatic rings, (4) number of chains, (5) number of rotatable bonds, (6) number of H-acceptors, (7) computed octanol/water partition coefficient (AlogP), and (8) number of nonconjugated C atoms in nonaromatic rings. Significant features of the CNS-TEMPO penalty score are the extension of the multiparameter approach to generate an accurate weight factor for each physicochemical property, the use of limits on both sides of the computed property space range during the penalty calculation, and the classification of CNS and non-CNS drug scores. CNS-TEMPO significantly outperformed CNS-MPO and the Schrödinger QikProp CNS parameter (QP_CNS) in evaluating CNS drugs and has been extensively applied in support of CNS lead optimization programs.

Central Nervous System Multiparameter Optimization Desirability: Application in Drug Discovery

Significant progress has been made in prospectively designing molecules using the central nervous system multiparameter optimization (CNS MPO) desirability tool, as evidenced by the analysis reported herein of a second wave of drug candidates that originated after the development and implementation of this tool. This simple-to-use design algorithm has expanded design space for CNS candidates and has further demonstrated the advantages of utilizing a flexible, multiparameter approach in drug discovery rather than individual parameters and hard cutoffs of physicochemical properties. The CNS MPO tool has helped to increase the percentage of compounds nominated for clinical development that exhibit alignment of ADME attributes, cross the blood-brain barrier, and reside in lower-risk safety space (low ClogP and high TPSA). The use of this tool has played a role in reducing the number of compounds submitted to exploratory toxicity studies and increasing the survival of our drug candidates through regulatory toxicology into First in Human studies. Overall, the CNS MPO algorithm has helped to improve the prioritization of design ideas and the quality of the compounds nominated for clinical development.

A new method to access triazole-fused spiro-guanidines from the reaction of isothiocyanates tethered N-sulfonyl-1,2,3-triazoles and amines

The reaction of isothiocyanate tethered N-sulfonyl-1,2,3-triazoles and amines afforded asymmetrical guanidines in fair to excellent yields through a two-component tandem reaction process.