Small Molecule Drug Discovery Research Articles

Discovery of 5-Azaquinoxaline Derivatives as Potent and Orally Bioavailable Allosteric SHP2 Inhibitors.

SHP2 has emerged as an important target for oncology small-molecule drug discovery. As a nonreceptor tyrosine phosphatase within the MAPK pathway, it has been shown to control cell growth, differentiation, and oncogenic transformation. We used structure-based design to find a novel class of potent and orally bioavailable SHP2 inhibitors. Our efforts led to the discovery of the 5-azaquinoxaline as a new core for developing this class of compounds. Optimization of the potency and properties of this scaffold generated compound 30, that exhibited potent in vitro SHP2 inhibition and showed excellent in vivo efficacy and pharmacokinetic profile.

Intrinsic Solubility of Ionizable Compounds from pKa Shift.

Aqueous solubility of pharmaceutical substances plays an important role in small molecule drug discovery and development, with ionizable groups often employed to enhance solubility. Drug candidate compounds often contain ionizable groups to increase their solubility. Recognizing that the electrostatically charged form of the compound is much more soluble than the uncharged form, this work proposes a model to explore the relationship between the pKa shift of the ionizable group and dissolution equilibria. The model considers three forms of a compound: dissolved-charged, dissolved-uncharged, and aggregated-uncharged. It analyzes two linked equilibria: the protonation of the ionizable group and the dissolution-aggregation of the uncharged form, with the observed pKa shift depending on the total concentration of the compound. The active concentration of the aggregates determines this shift. The model was explored through the determination of the pKa shift and intrinsic solubility of specific compounds, such as ICPD47, a high-affinity inhibitor of the Hsp90 chaperone protein and anticancer target, as well as benzoic acid and benzydamine. The model holds the potential for a more nuanced understanding of intrinsic solubility and may lead to advancements in drug discovery and development.

Screening approaches for the identification of Nrf2-Keap1 protein-protein interaction inhibitors targeting hot spot residues

Protein-protein interactions (PPIs) play a crucial role in most biological processes and are important targets in the development of therapeutic agents. However, small molecule drug discovery that targets PPIs remains very challenging. Targeting hot spot residues is considered the best option for inhibiting such interactions, but there are few examples of how knowledge of hot spots can be used in high throughput screening to find hit compounds. A substrate adaptor protein for a ubiquitin ligase complex, Kelch-like ECH-associated protein 1 (Keap1), negatively modulates the expression of genes involved in cellular protection against oxidative stress. Here, we focused on three arginine hot spot residues in the Keap1 substrate binding pocket (Arg380, Arg415, and Arg483), and screened the carboxylic acid library owned by Japan Tobacco Inc. for compounds that interact with the arginine residues in differential scanning fluorescence assays. Furthermore, we identified several small molecule compounds that specifically bind to the Keap1 Kelch domain hot spots by comparing binding to alanine mutant proteins (R380A, R415A, and R483A) with binding to the wild-type protein using surface plasmon resonance (SPR) screening. These compounds inhibited the protein-protein interaction between the Keap1 Kelch domain and the nuclear factor erythroid 2-related factor 2 (Nrf2) peptide, and the ubiquitination of Nrf2 catalyzed by the Cul3/RINGBox 1 E3 ligase. In addition, the binding mode of one compound (Compound 4) was determined by X-ray crystallography after validation of binding by isothermal titration calorimetry, native mass spectrometry, and nuclear magnetic resonance. Compound 4 had favorable thermodynamic properties, and noncovalently bound to Keap1 with a stoichiometry of 1:1. Our results suggest that Compound 4 could potentially be developed into effective therapeutic or preventive agents for a variety of diseases and conditions such as oxidative stress response, inflammation, and carcinogenesis. We believe that the use of a set of complementary biophysical techniques including the SPR assay with single alanine mutant of hot spots provides opportunities to identify hit compounds for developing inhibitors of PPIs.

Identification of 2,4-Diaminoquinazoline Derivative as a Potential Small-Molecule Inhibitor against Chikungunya and Ross River Viruses.

Alphaviruses are serious zoonotic threats responsible for significant morbidity, causing arthritis or encephalitis. So far, no licensed drugs or vaccines are available to combat alphaviral infections. About 300,000 chikungunya virus (CHIKV) infections have been reported in 2023, with more than 300 deaths, including reports of a few cases in the USA as well. The discovery and development of small-molecule drugs have been revolutionized over the last decade. Here, we employed a cell-based screening approach using a series of in-house small-molecule libraries to test for their ability to inhibit CHIKV replication. DCR 137, a quinazoline derivative, was found to be the most potent inhibitor of CHIKV replication in our screening assay. Both, the cytopathic effect, and immunofluorescence of infected cells were reduced in a dose-dependent manner with DCR 137 post-treatment. Most importantly, DCR 137 was more protective than the traditional ribavirin drug and reduced CHIKV plaque-forming units by several log units. CHIKV-E2 protein levels were also reduced in a dose-dependent manner. Further, DCR 137 was probed for its antiviral activity against another alphavirus, the Ross River virus, which revealed effective inhibition of viral replication. These results led to the identification of a potential quinazoline candidate for future optimization that might act as a pan-alphavirus inhibitor.

Is this a golden age of small-molecule drug discovery?

Is this a golden age of small-molecule drug discovery?

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science.

Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries.

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.

ZPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15.

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions. This module enables the extraction of protein-ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic-level protein-ligand feature encoding and fusion to output refined ligand poses and ligand per-atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF-2016 protein-ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein-ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side-chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein-ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

The Magic Methyl and Its Tricks in Drug Discovery and Development.

One of the key scientific aspects of small-molecule drug discovery and development is the analysis of the relationship between its chemical structure and biological activity. Understanding the effects that lead to significant changes in biological activity is of paramount importance for the rational design and optimization of bioactive molecules. The "methylation effect", or the "magic methyl" effect, is a factor that stands out due to the number of examples that demonstrate profound changes in either pharmacodynamic or pharmacokinetic properties. In many cases, this has been carried out rationally, but in others it has been the product of serendipitous observations. This paper summarizes recent examples that provide an overview of the current state of the art and contribute to a better understanding of the methylation effect in bioactive small-molecule drug candidates.

Design of Tracers in Fluorescence Polarization Assay for Extensive Application in Small Molecule Drug Discovery.

Development of fluorescence polarization (FP) assays, especially in a competitive manner, is a potent and mature tool for measuring the binding affinities of small molecules. This approach is suitable for high-throughput screening (HTS) for initial ligands and is also applicable for further study of the structure-activity relationships (SARs) of candidate compounds for drug discovery. Buffer and tracer, especially rational design of the tracer, play a vital role in an FP assay system. In this perspective, we provided different kinds of approaches for tracer design based on successful cases in recent years. We classified these tracers by different types of ligands in tracers, including peptide, nucleic acid, natural product, and small molecule. To make this technology accessible for more targets, we briefly described the basic theory and workflow, followed by highlighting the design and application of typical FP tracers from a perspective of medicinal chemistry.

Small molecule inhibitor of tau self-association in a mouse model of tauopathy: A preventive study in P301L tau JNPL3 mice.

Advances in tau biology and the difficulties of amyloid-directed immunotherapeutics have heightened interest in tau as a target for small molecule drug discovery for neurodegenerative diseases. Here, we evaluated OLX-07010, a small molecule inhibitor of tau self-association, for the prevention of tau aggregation. The primary endpoint of the study was statistically significant reduction of insoluble tau aggregates in treated JNPL3 mice compared with Vehicle-control mice. Secondary endpoints were dose-dependent reduction of insoluble tau aggregates, reduction of phosphorylated tau, and reduction of soluble tau. This study was performed in JNPL3 mice, which are representative of inherited forms of 4-repeat tauopathies with the P301L tau mutation (e.g., progressive supranuclear palsy and frontotemporal dementia). The P301L mutation makes tau prone to aggregation; therefore, JNPL3 mice present a more challenging target than mouse models of human tau without mutations. JNPL3 mice were treated from 3 to 7 months of age with Vehicle, 30 mg/kg compound dose, or 40 mg/kg compound dose. Biochemical methods were used to evaluate self-associated tau, insoluble tau aggregates, total tau, and phosphorylated tau in the hindbrain, cortex, and hippocampus. The Vehicle group had higher levels of insoluble tau in the hindbrain than the Baseline group; treatment with 40 mg/kg compound dose prevented this increase. In the cortex, the levels of insoluble tau were similar in the Baseline and Vehicle groups, indicating that the pathological phenotype of these mice was beginning to emerge at the study endpoint and that there was a delay in the development of the phenotype of the model as originally characterized. No drug-related adverse effects were observed during the 4-month treatment period.

Off-target pharmacological activity at various kinases: Potential functional and pathological side effects

In drug discovery, during the lead optimization and candidate characterization stages, novel small molecules are frequently evaluated in a battery of in vitro pharmacology assays to identify potential unintended, off-target interactions with various receptors, transporters, ion channels, and enzymes, including kinases. Furthermore, these screening panels may also provide utility at later stages of development to provide a mechanistic understanding of unexpected safety findings. Here, we present a compendium of the most likely functional and pathological outcomes associated with interaction(s) to a panel of 95 kinases based on an extensive curation of the scientific literature. This panel of kinases was designed by AbbVie based on safety-related data extracted from the literature, as well as from over 20 years of institutional knowledge generated from discovery efforts. For each kinase, the scientific literature was reviewed using online databases and the most often reported functional and pathological effects were summarized. This work should serve as a practical guide for small molecule drug discovery scientists and clinical investigators to predict and/or interpret adverse effects related to pharmacological interactions with these kinases.

WarpDOCK: Large-Scale Virtual Drug Discovery Using Cloud Infrastructure.

warpDOCK is an open-source pipeline for virtual small-molecule drug discovery using cloud infrastructure. warpDOCK is designed from the ground up for the Oracle Cloud Infrastructure (OCI), enabling harmonious parallelism of docking calculations over thousands to hundreds of thousands of cores. This enables cost-effective sampling of ultra-large chemical libraries, potentially reducing the time to identify lead drug candidates by orders of magnitude. By utilizing established docking software and automating each step of the process, warpDOCK makes large-scale virtual screening accessible to a broad user group. The warpDOCK code can be found at the BruningLab GitHub repository (https://github.com/BruningLab/warpDOCK).

Development of Orally Bioavailable Peptides Targeting an Intracellular Protein: From a Hit to a Clinical KRAS Inhibitor.

Cyclic peptides as a therapeutic modality are attracting a lot of attention due to their potential for oral absorption and accessibility to intracellular tough targets. Here, starting with a drug-like hit discovered using an mRNA display library, we describe a chemical optimization that led to the orally available clinical compound known as LUNA18, an 11-mer cyclic peptide inhibitor for the intracellular tough target RAS. The key findings are as follows: (i) two peptide side chains were identified that each increase RAS affinity over 10-fold; (ii) physico-chemical properties (PCP) including Clog P can be adjusted by side-chain modification to increase membrane permeability; (iii) restriction of cyclic peptide conformation works effectively to adjust PCP and improve bio-activity; (iv) cellular efficacy was observed in peptides with a permeability of around 0.4 × 10-6 cm/s or more in a Caco-2 permeability assay; and (v) while keeping the cyclic peptide's main-chain conformation, we found one example where the RAS protein structure was changed dramatically through induced-fit to our peptide side chain. This study demonstrates how the chemical optimization of bio-active peptides can be achieved without scaffold hopping, much like the processes for small molecule drug discovery that are guided by Lipinski's rule of five. Our approach provides a versatile new strategy for generating peptide drugs starting from drug-like hits.

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review.

In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

Molecular Determinants Involved in Candida albicans Biofilm Formation and Regulation.

Candida albicans is known for its pathogenicity, although it lives within the human body as a commensal member. The commensal nature of C. albicans is well controlled and regulated by the host's immune system as they live in the harmonized microenvironment. However, the development of certain unusual microhabitat conditions (change in pH, co-inhabiting microorganisms' population ratio, debilitated host-immune system) pokes this commensal fungus to transform into a pathogen in such a way that it starts to propagate very rapidly and tries to breach the epithelial barrier to enter the host's systemic circulations. In addition, Candida is infamous as a major nosocomial (hospital-acquired infection) agent because it enters the human body through venous catheters or medical prostheses. The hysterical mode of C. albicans growth builds its microcolony or biofilm, which is pathogenic for the host. Biofilms propose additional resistance mechanisms from host immunity or extracellular chemicals to aid their survival. Differential gene expressions and regulations within the biofilms cause altered morphology and metabolism. The genes associated with adhesiveness, hyphal/pseudo-hyphal growth, persister cell transformation, and biofilm formation by C. albicans are controlled by myriads of cell-signaling regulators. These genes' transcription is controlled by different molecular determinants like transcription factors and regulators. Therefore, this review has focused discussion on host-immune-sensing molecular determinants of Candida during biofilm formation, regulatory descriptors (secondary messengers, regulatory RNAs, transcription factors) of Candida involved in biofilm formation that could enable small-molecule drug discovery against these molecular determinants, and lead to disrupt the well-structured Candida biofilms effectively.

DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning

Targeting lncRNAs by small molecules (SM-lncR) to alter their expression levels has emerged as an important therapeutic modality for disease treatment. To date, no computational tools have been dedicated to predicting small molecule-mediated upregulation or downregulation of lncRNA expression. Here, we introduce DeepdlncUD, which integrates predictions of nine deep learning algorithms together, to infer the regulation types of small molecules on modulating lncRNA expression. Through systematic optimization on a training set of 771 upregulation and 739 downregulation SM-lncR pairs, each encoding 1369 sequence, representational, and physiochemical features, this method outperforms a recently released program, DeepsmirUD, by achieving 0.674 in AUC (area under the receiver operating characteristic curve), 0.722 in AUCPR (area under the precision-recall curve), 0.681 in F1-score, and 0.516 in Jaccard Index on a test set of 222 SM-lncR pairs. By extracting 125 upregulation and 46 downregulation SM-lncR pairs that involve disease-associated lncRNAs, DeepdlncUD is shown to gain an accuracy of 0.700 in the pathological context. Using connectivity scores, around half of the small molecules are correctly estimated as drugs to treat lncRNA-regulated diseases. This tool can be run at a fast speed to assist the discovery of potential small molecule drugs of lncRNA targets on a large scale. DeepdlncUD is publicly available at https://github.com/2003100127/deepdlncud.

Hypolipidemic Activity of Sesquiterpene Lactones and their Derivatives

BACKGROUND: Despite the available range of lipid-lowering drugs, mainly of synthetic origin, the problem of atherosclerosis therapy and its manifestations remain unresolved. Bioinformatics methods, in particular molecular docking, are considered as a promising direction in terms of developing effective original lipid-lowering drugs. Today, as a promising source of natural lipid-lowering agents, it is advisable to consider natural terpenoids, namely sesquiterpene lactones, which are distinguished by a wide range of pharmacological activity. This article presents the results of a virtual and biological screening of the lipid-lowering activity of sesquiterpene γ-lactones and their chemically modified derivatives. AIM: The aim is to evaluate the lipid-lowering properties of samples of sesquiterpene γ-lactones and their derivatives by virtual and biological screening methods. METHODS: Molecular modeling of the binding energy of the “ligand-target” complex (docking). Molecular docking of the lipid-lowering activity of sesquiterpene γ-lactones and their derivatives was performed using the Glide program from the developer of the Schrodinger Small-Molecule Drug Discovery package using the Extra Precision algorithm (https://www.schrodinger.com/glide). The effect of sesquiterpene lactones on the expression of genes for key enzymes of lipid metabolism in the liver was studied in an in vivo model of hyperlipidemia caused by an atherogenic diet. CONCLUSION: Virtual screening of the lipid-lowering activity of sesquiterpene γ-lactones and their derivatives by molecular docking revealed a number of promising compounds (matricin, matricarin, grossmisin oxime, austricin oxime, 5β (H)-austricin) receptor interactions on the enzyme system cholesterol 7α-hydroxylase (CYP7A1). New mechanisms of lipid-lowering activity for sesquiterpene γ-lactones were proposed, which were established in the study of gene expression of key enzymes of lipid metabolism in the liver in a model of hyperlipidemia caused by an atherogenic diet in rats under in vivo conditions.

Eyes on Topical Ocular Disposition: The Considered Design of a Lead Janus Kinase (JAK) Inhibitor That Utilizes a Unique Azetidin-3-Amino Bridging Scaffold to Attenuate Off-Target Kinase Activity, While Driving Potency and Aqueous Solubility.

An unmet medical need remains for patients suffering from dry eye disease (DED). A fast-acting, better-tolerated noncorticosteroid anti-inflammatory eye drop could improve patient outcomes and quality of life. Herein, we describe a small-molecule drug discovery effort to identify novel, potent, and water-soluble JAK inhibitors as immunomodulating agents for topical ocular disposition. A focused library of known 3-(4-(2-(arylamino)pyrimidin-4-yl)-1H-pyrazol-1-yl)propanenitriles was evaluated as a molecular starting point. Structure-activity relationships (SARs) revealed a ligand-efficient (LE) JAK inhibitor series, amenable to aqueous solubility. Subsequent in vitro analysis indicated the potential for off-target toxicity. A KINOMEscan selectivity profile of 5 substantiated the likelihood of widespread series affinity across the human kinome. An sp2-to-sp3 drug design strategy was undertaken to attenuate off-target kinase activity while driving JAK-STAT potency and aqueous solubility. Tactics to reduce aromatic character, increase fraction sp3 (Fsp3), and bolster molecular complexity led to the azetidin-3-amino bridging scaffold in 31.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint).

Hepatocyte intrinsic clearance (CLint) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CLp) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CLp IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (fu,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CLp IVIVE approach. Results show that predictions of CLp are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Å2, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CLp were better for compounds with moderate-to-high hepatocyte CLint (>10 μL/min/106 cells), high passive cellular permeability (Papp > 100 nm/s), and moderate observed CLp (5-50 mL/min/kg). Influences of plasma protein binding (fu,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (Papp > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CLp IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CLp IVIVE. Our results indicate that the current practice of prospective CLp IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CLp IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CLp IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.