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© 2015 Ekins et al.Background Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. Methodol

Carvedilol is an established anti-hypertensive medication used as one of the cornerstones of congestive heart failure. In addition to its cardiac remodeling properties, recent interest has been shown in the field of cancer mutagenesis. Inhibition of various proteins involved in proliferation pathways in cancer formation has shown potential therapeutic applications in protecting against apoptosis. Various clinical trials have recently been employed to study potential inhibitors of poly (ADP-ribose) polymerase 1, a well-known protein involved in cancer mutagenesis. Our study suggests a potential inhibitory interaction with polymerase 1and Carvedilol. By using virtual screening target software, we hope to identify a potential pharmacological interaction between Carvedilol and various well known cancer genes, which can serve as possible therapeutic targets and potential chemo-preventative agents. We used PyRx, which is a virtual screening and docking software program, to screen proteins known to be involved in DNA repair, cancer prevention, and programmed cell death pathways and measure their binding affinities with Carvedilol. Among the various proteins tested with our virtual screening program, Carvedilol showed the highest binding affinity of -8.7 kcal/mol, to polymerase 1as well as with the receptor tyrosineprotein kinase erb-2, (HER2/neu), often found frequently in breast cancer (-8.3 kcal/mol) and also with the serine protein kinase ATM protein, involved in Ataxia Telangiectasia with a high binding affinity of -8.8 kcal/mol. Although numerous anti-cancer mechanisms have been postulated and appropriate pharmaceutical agents have been designed which have decreased mortality, unwanted toxicity and cost has remained a limiting factor. Recent studies have focused on particular cancer mutagenesis pathways such as the role of polymerase 1 and proliferation in certain cancers. Our studies suggest a significant binding and inhibition of polymerase 1 with Carvedilol. This presents a novel and potential therapeutic role as a cost effective and readily available cancer treatment as well as possible chemo-preventative medication in certain cancers with little or no effective treatments. [ABSTRACT FROM AUTHOR]

Background: The third version of the National Asthma Education and Prevention Program (NAEPP) Expert Panel Report (EPR-3): Guidelines on the Diagnosis and Management of Asthma emphasizes the need to use asthma control rather than patient severity to base adjustments to treatment and ultimately improve patient outcomes. The objectives of the current study were to assess control of patients with moderate-to-severe asthma, examine the natural history of the disease, practice patterns and resource utilization in specialty community practices according to recently reviewed NAEPP guidelines.Research Design and Methods: This analysis represents a retrospective, multicenter, randomized study of 1009 patient charts in sixty United States allergy and pulmonary medicine community practices. The proportion of patients with controlled and uncontrolled asthma over 12 months, prevalence and characteristics of atopy, past asthma history, pulmonary function, medications and treatment patterns, patient and clinical practice characteristics were analyzed.Main Outcome Measures: The primary outcome of interest was asthma control.Results: A total of 365 male and 644 female patients with moderate-to-severe persistent asthma (mean 43.2 +/- 17.1 years) were enrolled. 81.9% of patients were uncontrolled according to recent NAEPP guidelines. Importantly, a greater percentage of patients with moderate asthma vs. severe persistent asthma were uncontrolled (p < 0.0114). Atopy was detected in 92% of patients. Patients with early onset of asthma were associated with control (p < 0.0433). Atopic symptoms, such as allergic rhinitis (p < 0.0130) and rhinosinusitis (p < 0.0476), were associated with uncontrolled asthma. Uncontrolled patients were also associated with more medications (a mean of 4.05 +/- 1.87 medications) than were controlled patients (a mean of 3.40 +/- 1.37 medications (p < 0.0001), although the temporal relationship of this association was not recorded. Limitations may have included patient and/or study site selection bias and difficulty in the process of operationalizing the definitions of control and disease severity. Since the current study only examined patients from specialty practices, the results may not be generalizable to the overall asthma population.Conclusions: Greater than 80% of asthma patients from specialty practices were uncontrolled with regard to asthma symptoms. Atopic symptoms, such as allergic rhinitis and rhinosinusitis, in addition to a greater number of medications, were associated with uncontrolled asthma. Moreover, patients designated as having asthma of moderate severity were associated with being uncontrolled more than were those with severe asthma (p < 0.0114), which suggests that the former population may not have received adequate assessment of impairment or risk, with subsequent changes in treatment for control of symptoms. [ABSTRACT FROM AUTHOR]

Abstract With the advent of next-generation DNA sequencing, the pace of inherited orphan disease gene identification has increased dramatically, a situation that will continue for at least the next several years. At present, the numbers of such identified disease genes significantly outstrips the number of laboratories available to investigate a given disorder, an asymmetry that will only increase over time. The hope for any genetic disorder is, where possible and in addition to accurate diagnostic test formulation, the development of therapeutic approaches. To this end, we propose here the development of a strategic toolbox and preclinical research pathway for inherited orphan disease. Taking much of what has been learned from rare genetic disease research over the past two decades, we propose generalizable methods utilizing transcriptomic, system-wide chemical biology datasets combined with chemical informatics and, where possible, repurposing of FDA approved drugs for pre-clinical orphan disease therapies. It is hoped that this approach may be of utility for the broader orphan disease research community and provide funding organizations and patient advocacy groups with suggestions for the optimal path forward. In addition to enabling academic pre-clinical research, strategies such as this may also aid in seeding startup companies, as well as further engaging the pharmaceutical industry in the treatment of rare genetic disease.

Abstract Background Laboratory tests for routine drug of abuse and toxicology (DOA/Tox) screening, often used in emergency medicine, generally utilize antibody-based tests (immunoassays) to detect classes of drugs such as amphetamines, barbiturates, benzodiazepines, opiates, and tricyclic antidepressants, or individual drugs such as cocaine, methadone, and phencyclidine. A key factor in assay sensitivity and specificity is the drugs or drug metabolites that were used as antigenic targets to generate the assay antibodies. All DOA/Tox screening immunoassays can be limited by false positives caused by cross-reactivity from structurally related compounds. For immunoassays targeted at a particular class of drugs, there can also be false negatives if there is failure to detect some drugs or their metabolites within that class. Methods Molecular similarity analysis, a computational method commonly used in drug discovery, was used to calculate structural similarity of a wide range of clinically relevant compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target ('antigenic') molecules of DOA/Tox screening tests. These results were compared with cross-reactivity data in the package inserts of immunoassays marketed for clinical testing. The causes for false positives for phencyclidine and tricyclic antidepressant screening immunoassays were investigated at the authors' medical center using gas chromatography/mass spectrometry as a confirmatory method. Results The results illustrate three major challenges for routine DOA/Tox screening immunoassays used in emergency medicine. First, for some classes of drugs, the structural diversity of common drugs within each class has been increasing, thereby making it difficult for a single assay to detect all compounds without compromising specificity. Second, for some screening assays, common 'out-of-class' drugs may be structurally similar to the target compound so that they account for a high frequency of false positives. Illustrating this point, at the authors' medical center, the majority of positive screening results for phencyclidine and tricyclic antidepressants assays were explained by out-of-class drugs. Third, different manufacturers have adopted varying approaches to marketed immunoassays, leading to substantial inter-assay variability. Conclusion The expanding structural diversity of drugs presents a difficult challenge for routine DOA/Tox screening that limit the clinical utility of these tests in the emergency medicine setting.

Abstract Background The pregnane X receptor (PXR) shows the highest degree of cross-species sequence diversity of any of the vertebrate nuclear hormone receptors. In this study, we determined the pharmacophores for activation of human, mouse, rat, rabbit, chicken, and zebrafish PXRs, using a common set of sixteen ligands. In addition, we compared in detail the selectivity of human and zebrafish PXRs for steroidal compounds and xenobiotics. The ligand activation properties of the Western clawed frog (Xenopus tropicalis) PXR and that of a putative vitamin D receptor (VDR)/PXR cloned in this study from the chordate invertebrate sea squirt (Ciona intestinalis) were also investigated. Results Using a common set of ligands, human, mouse, and rat PXRs share structurally similar pharmacophores consisting of hydrophobic features and widely spaced excluded volumes indicative of large binding pockets. Zebrafish PXR has the most sterically constrained pharmacophore of the PXRs analyzed, suggesting a smaller ligand-binding pocket than the other PXRs. Chicken PXR possesses a symmetrical pharmacophore with four hydrophobes, a hydrogen bond acceptor, as well as excluded volumes. Comparison of human and zebrafish PXRs for a wide range of possible activators revealed that zebrafish PXR is activated by a subset of human PXR agonists. The Ciona VDR/PXR showed low sequence identity to vertebrate VDRs and PXRs in the ligand-binding domain and was preferentially activated by planar xenobiotics including 6-formylindolo-[3,2-b]carbazole. Lastly, the Western clawed frog (Xenopus tropicalis) PXR was insensitive to vitamins and steroidal compounds and was activated only by benzoates. Conclusion In contrast to other nuclear hormone receptors, PXRs show significant differences in ligand specificity across species. By pharmacophore analysis, certain PXRs share similar features such as human, mouse, and rat PXRs, suggesting overlap of function and perhaps common evolutionary forces. The Western clawed frog PXR, like that described for African clawed frog PXRs, has diverged considerably in ligand selectivity from fish, bird, and mammalian PXRs.

Influenza viruses that cause seasonal and pandemic flu are a permanent health threat. The surface glycoprotein, neuraminidase, is crucial for the infectivity of the virus and therefore an attractive target for flu drug discovery campaigns. We have designed and synthesized more than 40 3-indolinone derivatives. We mainly investigated the role of substituents at the 2 position of the core as well as the introduction of substituents or a nitrogen atom in the fused phenyl ring of the core for inhibition of influenza virus neuraminidase activity and replication in vitro and in vivo. After evaluating the compounds for their ability to inhibit the viral neuraminidase, six potent inhibitors 3c, 3e, 7c, 12o, 12v, 18d were progressed to evaluate for cytotoxicity and inhibition of influenza virus A/PR/8/34 replication in in MDCK cells. Two hit compounds 3e and 12o were tested in an animal model of influenza virus infection. Molecular mechanism of the 3-indolinone derivatives interactions with the neuraminidase was revealed in molecular dynamic simulations. Proposed inhibitors bind to the 430-cavity that is different from the conventional binding site of commercial compounds. The most promising 3-indolinone inhibitors demonstrate stronger interactions with the neuraminidase in molecular models that supports proposed binding site., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

The development of new drugs addressing serious mental health and other disorders should avoid the psychedelic experience. Analogs of psychedelic drugs can have clinical utility and are termed "psychoplastogens". These represent promising candidates for treating opioid use disorder to reduce drug dependence, with rarely reported serious adverse effects. This drug abuse cessation is linked to the induction of neuritogenesis and increased neuroplasticity, a hallmark of psychedelic molecules, such as lysergic acid diethylamine. Some, but not all psychoplastogens may act through the G-protein coupled receptor (GPCR) 5HT 2A whereas others may display very different polypharmacology making prediction of hallucinogenic potential challenging. In the process of developing tools to help design new psychoplastogens, we have used artificial intelligence in the form of machine learning classification models for predicting psychedelic effects using a published in vitro data set from PsychLight (support vector classification (SVC), area under the curve (AUC) 0.74) and in vivo human data derived from books from Shulgin and Shulgin (SVC, AUC, 0.72) with nested five-fold cross validation. We have also explored conformal predictors with ECFP6 and electrostatic descriptors in an effort to optimize them. These models have been used to predict known 5HT 2A agonists to assess their potential to act as psychedelics and induce hallucinations for PsychLight (SVC, AUC 0.97) and Shulgin and Shulgin (random forest, AUC 0.71). We have tested these models with head twitch data from the mouse. This predictive capability is desirable to reliably design new psychoplastogens that lack in vivo hallucinogenic potential and help assess existing and future molecules for this potential. These efforts also provide useful insights into understanding the psychedelic structure activity relationship.

The eukaryotic parasite Trypanosoma cruzi ( T. cruzi ) is responsible for Chagas disease, which results in heart failure in patients. The disease is more common in Latin America, and is an emerging infection with The Centers for Disease Control estimating that greater than 300,000 people are currently infected in the United States. This disease has also spread from South and Central America, where it is endemic to many other countries, including Australia, Japan, and Spain. Current therapy for Chagas disease is inadequate due to limited efficacy in the indeterminate and chronic phases of the disease, in addition to the adverse effects from nifurtimox and benznidazole, which are nitro-containing drugs used for therapy. There is a clear need for new therapies for the Chagas disease. Using a computational machine learning approach, we have previously shown that the antimalarial pyronaridine tetraphosphate is active against T. cruzi Brazil-luc in vitro against parasites infecting a myoblast cell line and is also active in vivo in an acute mouse model of Chagas disease when dosed i.p. We now further evaluated oral pyronaridine as a monotherapy to determine the minimum effective dose to treat acute and chronic models of Chagas disease. Our results for T. cruzi Brazil-luc demonstrated daily oral dosing with pyronaridine from 150 to 600 mg/kg resulted in statistically significant inhibition in the 7 day acute mouse model. Combination therapy with daily dosing of benznidazole and pyronaridine in the acute infection model demonstrated that 300 mg/kg pyronaridine could return statistically significant antiparasitic activity to a subtherapetic 10 mg/kg benznidazole. In contrast, pyronaridine as monotherapy or combined with benznidazole lacked efficacy in the chronic mouse model, whereas 100 mg/kg benznidazole alone demonstrated undetectable parasites in the heart of mice. Pyronaridine requires further assessment in other chronic models to identify if it can be used beyond the acute stage of T. cruzi infection., Competing Interests: The authors declare the following competing financial interest(s): S.E. is owner of Collaborations Pharmaceuticals Inc., and T.R.L. is an employee at Collaborations Pharmaceuticals Inc. All other authors are associates of the University of California, San Diego., (© 2024 The Authors. Published by American Chemical Society.)

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset., (© 2024. The Author(s).)

Venomous agent X (VX) is an organophosphate acetylcholinesterase (AChE) inhibitor, and although it is one of the most toxic AChE inhibitors known, the extent of metabolism in humans is not currently well understood. The known metabolism in humans is limited to the metabolite identification from a single victim of the Osaka poisoning in 1994, which allowed for the identification of several metabolic products. VX has been reported to be metabolized in vitro by paraoxonase-1 and phosphotriesterase, although their binding constants are many orders of magnitude above the LD 50 , suggesting limited physiologic relevance. Using incubation with human liver microsomes (HLMs), we have now characterized the metabolism of VX and the formation of multiple metabolites as well as identified a Food and Drug Administration-approved drug [ethylenediaminetetraacetic acid (EDTA)] that enhances the metabolic rate. HLM incubation alone shows a pronounced increase in the metabolism of VX compared with buffer, suggesting that cytochrome P450-mediated metabolism of VX is occurring. We identified a biphasic decay with two distinct rates of metabolism. The enhancement of VX metabolism in multiple buffers was assessed to attempt to mitigate the effect of hydrolysis rates. The formation of VX metabolites was shown to be shifted with HLMs, suggesting a pathway enhancement over simple hydrolysis. Additionally, our investigation of hydrolysis rates in various common buffers used in biologic assays discovered dramatic differences in VX stability. The new human in vitro VX metabolic data reported points to a potential in vivo treatment strategy (EDTA) for rescue in individuals that are poisoned though enhancement of metabolism alongside existing treatments. SIGNIFICANCE STATEMENT: Venomous agent X (VX) is a potent acetylcholinesterase inhibitor and chemical weapon. To date, we do not possess a clear understanding of its metabolism in humans that would assist us in treating those exposed to it. This study now describes the human liver microsomal metabolism of VX and identifies ethylenediaminetetraacetic acid, which appears to enhance the rate of metabolism. This may provide a potential treatment option for human VX poisoning., (Copyright © 2024 by The American Society for Pharmacology and Experimental Therapeutics.)

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC 90 2.95 μM; M. abscessus IC 90 59.1 μM) and tribromsalan (M. tuberculosis IC 90 76.92 μM; M. abscessus IC 90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism., Competing Interests: Declaration of competing interest S.E. is owner, and A.C.P. and T.R.L. are employees of Collaborations Pharmaceuticals, Inc. J.J.Y., D.J.W., Y.D., B.F., and J.L.D., are employees, and D.J.W., B.F., R.K., and S.L., are board members, of Data2Discovery, Inc., which has applied for patent protections covering discoveries described herein. All others have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs in silico , optimizing for precision over all other metrics. We compare state-of-the-art supervised contrastive learning (CL) with deep learning (DL) and Random Forest (RF) machine learning, across single and sequential modeling configurations, to identify the best models for BChE selectivity. We used these models to virtually screen a vendor library of 5 million compounds for BIs and tested 20 of these compounds in vitro . Seven of the 20 compounds displayed selectivity for BChE over AChE, reflecting a hit rate of 35% for our model predictions, suggesting a highly efficient strategy for modeling selective inhibition.

The P2Y 6 receptor, activated by uridine diphosphate (UDP), is a target for antagonists in inflammatory, neurodegenerative, and metabolic disorders, yet few potent and selective antagonists are known to date. This prompted us to use machine learning as a novel approach to aid ligand discovery, with pharmacological evaluation at three P2YR subtypes: initially P2Y 6 and subsequently P2Y 1 and P2Y 14 . Relying on extensive published data for P2Y 6 R agonists, we generated and validated an array of classification machine learning model using the algorithms deep learning (DL), adaboost classifier (ada), Bernoulli NB (bnb), k-nearest neighbors (kNN) classifier, logistic regression (lreg), random forest classifier (rf), support vector classification (SVC), and XGBoost (XGB) classifier models, and the common consensus was applied to molecular selection of 21 diverse structures. Compounds were screened using human P2Y 6 R-induced functional calcium transients in transfected 1321N1 astrocytoma cells and fluorescent binding inhibition at closely related hP2Y 14 R expressed in CHO cells. The hit compound ABBV-744, an experimental anticancer drug with a 6-methyl-7-oxo-6,7-dihydro-1H-pyrrolo[2,3-c]pyridine scaffold, had multifaceted interactions with the P2YR family: hP2Y 6 R inhibition in a non-surmountable fashion, suggesting that noncompetitive antagonism, and hP2Y 1 R enhancement, but not hP2Y 14 R binding inhibition. Other machine learning-selected compounds were either weak (experimental anti-asthmatic drug AZD5423 with a phenyl-1H-indazole scaffold) or inactive in inhibiting the hP2Y 6 R. Experimental drugs TAK-593 and GSK1070916 (100 µM) inhibited P2Y 14 R fluorescent binding by 50% and 38%, respectively, and all other compounds by < 20%. Thus, machine learning has led the way toward revealing previously unknown modulators of several P2YR subtypes that have varied effects., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Palmitoyl-protein thioesterase 1 (PPT1) is an understudied enzyme that is gaining attention due to its role in the depalmitoylation of several proteins involved in neurodegenerative diseases and cancer. PPT1 is overexpressed in several cancers, specifically cholangiocarcinoma and esophageal cancers. Inhibitors of PPT1 lead to cell death and have been shown to enhance the killing of tumor cells alongside known chemotherapeutics. PPT1 is hence a viable target for anticancer drug development. Furthermore, mutations in PPT1 cause a lysosomal storage disorder called infantile neuronal ceroid lipofuscinosis (CLN1 disease). Molecules that can inhibit, stabilize, or modulate the activity of this target are needed to address these diseases. We used PPT1 enzymatic assays to identify molecules that were subsequently tested by using differential scanning fluorimetry and microscale thermophoresis. Selected compounds were also tested in neuroblastoma cell lines. The resulting PPT1 screening data was used for building machine learning models to help select additional compounds for testing. We discovered two of the most potent PPT1 inhibitors reported to date, orlistat (IC 50 178.8 nM) and palmostatin B (IC 50 11.8 nM). When tested in HepG2 cells, it was found that these molecules had decreased activity, indicating that they were likely not penetrating the cells. The combination of in vitro enzymatic and biophysical assays enabled the identification of several molecules that can bind or inhibit PPT1 and may aid in the discovery of modulators or chaperones. The molecules identified could be used as a starting point for further optimization as treatments for other potential therapeutic applications outside CLN1 disease, such as cancer and neurological diseases., Competing Interests: The authors declare the following competing financial interest(s): S.E. is the owner and A.C.P., R.R., and E.M. are employees of Collaborations Pharmaceuticals, Inc., (© 2024 The Authors. Published by American Chemical Society.)