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Allosteric inhibition of JAK2 with lysine-reactive compounds that bind the pseudokinase domain.
No full textThe V617F mutation in the pseudokinase (JH2) domain of JAK2 is a frequent cause of myeloproliferative neoplasms (MPNs) and JAK2 inhibitors are an important therapeutic option for patients with these conditions. Currently approved JAK2 inhibitors target the kinase domain of JAK2, and while they exhibit varying degrees of selectivity among the four members of the JAK kinase family and across the kinome, all are equipotent against wild-type and V617F-mutant JAK2. Inhibition of WT JAK2 and other family members limits tolerability and therefore efficacy of current agents, making development of a mutant-selective JAK2 inhibitor a long-sought goal. Because the pseudokinase domain regulates the activity of the kinase domain and is the site of the V617F mutation, it represents a potential target for development of mutant-selective JAK2 inhibitors. Here we describe compounds that covalently bind the ATP-site of the JAK2 pseudokinase domain by targeting either Cys675 or Lys677, residues that are unique to the pseudokinase domain. In purified enzyme assays, we find that selected compounds potently inhibit pseudokinase-containing constructs with relative sparing of the isolated kinase domain, indicative of an allosteric mechanism of inhibition. Compound 20 (JH-XVII-135-2) incorporates a lysine-reactive fluorosulfate warhead and inhibits full-length JAK2 with an IC50 of 160 nM. A co-crystal structure of 20 with the JAK2 pseudokinase reveals its binding mode and confirms covalent modification of Lys677, which was also observed using LC-MS/MS. Though not mutant-selective in our biochemical assays, 20 demonstrates proof-of-concept for allosteric inhibition of JAK2 via the pseudokinase domain and for covalent targeting of JAK2 on Lys677
Some Common Dose-Exposure-Response Estimands and Conditions for Their Causal Identifiability.
No full textExposure-response analyses are central to dose selection in drug development. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose-exposure-response analyses. For simulated example studies inspired by real-world scenarios, we define dose-response estimands of clinical interest. The estimands are formalized using the potential outcome notation. Assumptions on the setup of the studies and the relation between treatment, exposure and response are expressed as a directed acyclic graph (DAG). The estimand is transformed using the assumption into expressions to identify the estimand based on the observed data. Three types of expressions are obtained. First, a pooled dose-exposure-response (DER) analysis that corresponds to a standard DER analysis as executed for many projects. Second, a pooled, covariate adjusted dose-response (DR) analysis, and third summaries of the outcomes in each randomized cohort. In our example, DER provides more precise estimates than DR as judged by the mean square error (MSE) of repeated simulation estimation. This work advances methodological rigor in DER analyses by integrating with causal inference methodologies and the estimand framework, enabling clearer interpretation of modeling assumptions and results. This has important concrete advantages. We obtain different estimation methods for the same estimand that may be compared to validate them. The potential for bias in the different estimation methods can be formally assessed. The proposed approach provides a generalizable strategy to improve exposure-response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies
Asymmetric C-H Functionalization of Bicyclo[2.1.1]hexanes and Their 2-Oxa- and 2-Aza Derivatives via Rhodium Carbene Intermediates.
No full textBicyclo[2.1.1]hexanes have generated considerable interest in recent years as bioisosteres of benzene. In this article, a C-H functionalization approach is described to derivatize the bicyclo[2.1.1]hexanes. The approach relies on dirhodium-catalyzed C-H insertion by donor/acceptor carbenes, which proceeds in a highly diastereoselective and enantioselective manner. By the appropriate choice of substrates, the reaction can also be highly site-selective. The bicyclo[2.1.1]hexane is a difficult system for C-H functionalization via carbene intermediates because it is a strained molecule, which causes the C-H bonds to be stronger than in an unstrained system. The only catalyst that performed well in this transformation is the newly developed D4 symmetric catalyst, Rh2(S-megaBNP)4, which contains four (4,4'-dichloro-6,6'-di(3,5-di-tert-butyl)phenyl)binaphthyl phosphate ligands. Computational studies revealed that the donor-acceptor carbene binds in a defined cleft within the bowl-shape of the dirhodium catalyst. Due to the high symmetry of the catalyst, only two orientations of the carbene are possible, and the most stable one has an open face for attack by the substrate. The substrate also needs to approach through a defined cleft causing the reaction to proceed with high levels of diastereoselectivity and enantioselectivity. These studies represent a further example of how the dirhodium catalysts can display many of the characteristics typically associated with enzymes with well-defined secondary interactions between the wall of the catalyst and the approaching substrate controlling the stereochemical outcome
Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.
No full textDrug discovery and medicinal chemistry efforts are increasingly influenced by machine learning (ML), with compound property prediction as a central application. ML models have demonstrated strong performance in predicting various compound properties from chemical structure. However, these models can exhibit varying levels of prediction error, making uncertainty quantification (UQ) essential for informed decisions. Standard UQ metrics include the distance to the molecules in the training set and prediction variance, obtained through methods such as model ensembles or Bayesian modeling. Although several UQ methodologies have been developed in recent years, no single approach consistently outperformed others. Herein, we present a comprehensive benchmark of UQ strategies for ML-based prediction of absorption, distribution, metabolism, and excretion (ADME) properties, using both in-house and public data sets. We employed the recently introduced UNIQUE (UNcertaInty QUantification bEnchmarking) framework and evaluated UQ method performance under data shifts. Our findings indicate data-based UQ metrics (e.g., chemical distance), and model-based UQ metrics (e.g., predicted value and variance) may capture complementary aspects of uncertainty. Their combination through error models, designed to predict the original ML model's error, yielded higher-quality uncertainty estimates. These error models emerged as a promising strategy for enhancing UQ, showing robustness in under various degrees and types of data shift. Taken together, our work highlights the potential of combining diverse UQ metrics and error modeling to improve reliability in molecular property prediction. By establishing standardized evaluation setups and assessing UQ under data shifts, we provide a foundation for future UQ method development and benchmarking in the field
Charcot–Marie–Tooth Type 1 A (CMT1A) is a hereditary neuropathy caused by a duplication of the peripheral myelin protein 22 (PMP22) gene. Emerging evidence suggests that lipid metabolism plays a central role in CMT1A pathology. This study investigated metabolic profiles in sciatic nerve tissue and plasma of PMP22 transgenic (TG) and wild-type (WT) rats at 2, 4, and 6 months of age. Utilizing targeted metabolomics, more than 600 metabolites covering central metabolic pathways and major lipid classes were analyzed, revealing distinct age-dependent changes in metabolic pathways. Alterations that emerged early and became increasingly pronounced with age were observed in sphingolipids and glycerophospholipids, while changes in other metabolic pathways, such as amino acids, storage lipids, bile acids, and nucleotide metabolism, were age-specific. Notably, in contrast to these age-dependent adaptive changes, three lipid signatures were identified that remained stable from the earliest age examined. These include: (1) an elevated ratio of hydroxylated to nonhydroxylated sphingolipids, (2) a reduced ratio of monounsaturated-containing to saturated fatty acid containing phosphatidylcholines, and (3) a decreased ratio of hexosylceramides to ceramides. Imaging mass spectrometry analyses confirmed disruptions in sphingolipid metabolism. These findings suggest a key regulatory role of PMP22 in lipid metabolism, as demonstrated by the early stabilization of specific lipid signatures compared to other metabolic changes that occurred in an age-dependent and adaptive manner. These observations provide valuable insights into the pathogenic mechanisms underlying CMT1A.
No full textCharcot-Marie-Tooth Type 1A (CMT1A) is a hereditary neuropathy caused by mutations in the peripheral myelin protein 22 (PMP22) gene. Emerging evidence suggests that lipid metabolism plays a central role in CMT1A pathology. This study investigates metabolic profiles in sciatic nerve tissue and plasma of PMP22 transgenic (TG) and wild type (WT) rats at 2, 4, and 6 months. Utilizing targeted metabolomics, more than 600 metabolites covering central metabolic pathways and major lipid classes were analyzed, revealing distinct age-dependent changes in metabolic pathways. Alterations that emerged early and became progressively more pronounced with age were observed in sphingo- and glycerophospholipids, followed later by changes in amino acids and storage lipids, while other metabolic pathways, such as bile acids and microbial metabolites, are age specific. Notably, unlike these age-dependent adaptive changes, three lipid signatures were identified that remained stable from the earliest age examined. These include: 1) a reduced ratio of mono-unsaturated to saturated fatty acids in phosphatidylcholines, 2) an elevated ratio of hydroxylated to non-hydroxylated sphingolipids, and 3) a decreased ratio of hexosylceramides to ceramides. Imaging mass spectrometry confirmed disruptions in sphingolipid metabolism. These findings suggest a key regulatory role of PMP22 in lipid metabolism, as evidenced by the early stabilization of specific lipid signatures compared to other metabolic changes that occurred in an age-dependent adaptive manner. These observations provide valuable insights into the pathogenic mechanisms underlying CMT1A
Reimagining Early-Phase Clinical Development in Japan: From Regulatory Obligation to Global Acceleration.
No full textJapan has faced persistent challenges of "Drug Lag" and "Drug Loss", partly due to the regulatory requirement for Japanese Phase I studies prior to global trial participation. However, recent regulatory reforms have introduced flexibility, creating new opportunities for Japan to strategically contribute to global drug development. This study redefined the value of Japanese Phase I by evaluating three options during the early development phase: the Japanese Phase I waiver, the first-in-human study conducted in Japan, and multifunctional Japanese Phase I studies. We analyzed 12 internal cases of Japanese Phase I waiver consultations and conducted a nationwide survey at early phase clinical trial sites. Our findings highlight Japan's robust clinical trial infrastructure for early phase trials. Japanese clinical trial sites have not only accumulated extensive experience in early phase trials but have also conducted specialized evaluations and enrolled diverse populations (e.g., non-Japanese Asians, Caucasians, and patients). The cycle time analysis showed that trials in Japan could be initiated within globally competitive timelines, often faster than those in the EU. These strengths position Japan as a key location for first-in-human and early phase trials, enabling earlier access to investigational therapies and supporting global development strategies. We propose a flexible, case-by-case approach that leverages Japan's clinical research capabilities. This strategy not only preserves Japan's clinical trial infrastructure but also aligns with national initiatives to strengthen the "Drug-Discovery Ecosystem". By integrating Japan into early phase development, pharmaceutical companies can accelerate global innovation while improving access to Japanese patients
Assessment and Control of Host Cell Proteins in Biologics: Survey of Industry Practices and a Vision for Harmonization.
No full textHost cell proteins (HCPs) are important process-related impurities produced by the host organism during the manufacturing of biotherapeutics. Even trace amounts of these contaminants can be considered significant during drug development due to their potential impact on the quality, safety, and/or efficacy of the therapeutic. This article summarizes the findings of a survey conducted by the IQ DruSafe Impurities Safety Working Group (Biologics Impurities Subteam) concerning industry practices and challenges related to HCPs in biologic therapeutics. The survey addressed four key areas: the scope of HCP control challenges, practices for HCP control and monitoring, methods for qualification of HCP levels, and regulatory interactions. Results revealed both perceived risks and experienced impact from HCP impurities as well as analytical strategies for their identification and quantification. The article also presents current default limits being employed for total and individual HCP impurities, approaches for assessing the safety and immunogenicity risk of HCPs, and a summary of feedback from global health authorities. Overall, the survey results illustrate progress in HCP management across biologic drug development while underscoring persistent challenges. The findings point to emerging best practices informed by historical knowledge and also reveal areas where a harmonized approach may be justified. Identifying and addressing challenges will require sustained industry collaboration and ongoing engagement with regulatory authorities to ensure the continued advancement of safe, effective biologic therapeutics
Cell Culture Media Release Using Inline Raman Spectroscopy and Artificial Neural Networks
No full textEnsuring the quality and consistency of cell culture media is essential in biopharmaceutical manufacturing. This study investigates the application of inline Raman spectroscopy combined with machine learning algorithms for real-time characterization and release of cell culture media compositions. Raman spectroscopy, known for its ability to provide detailed molecular fingerprints through inelastic scattering, enables the noninvasive identification and quantification of Raman-active media components and the indirect estimation of certain non-Raman-active quality markers via correlation-based models. Our methodology involved the collection of Raman spectra from media mixtures with varying compositions, systematically altered through two experimental designs. These spectra were preprocessed and used to train Artificial Neural Networks (ANNs), which accurately predicted critical media markers based on both direct Raman signals and indirect correlations with Raman-detectable species, achieving R2 values of 0.988 (glucose), 0.985 (glutamine), 0.994 (osmolality), 0.994 (potassium), and 0.975 (sodium). Subsequently, K-Nearest Neighbors (KNN) models were employed to classify the media based on solution composition ranges. The KNN models achieved approximately 90% accuracy in classifying solution ranges, showcasing the potential of this combined approach for inline, real-time quality control of continous media preparations. This study underscores the effectiveness of integrating Raman spectroscopy and machine learning models within the Process Analytical Technology (PAT) framework to enhance media release and quality assurance in biopharmaceutical manufacturing
Olink Proteomic Profiling of Vitreous Humor and Plasma From Proliferative Diabetic Retinopathy Patients Identifies a Novel Inflammatory Molecular Endotype.
No full textTo characterize proteomic profiles and underlying biological processes in vitreous humor and plasma of patients with proliferative diabetic retinopathy (PDR), diabetic patients without retinopathy (Non_DR), and non-diabetes mellitus patients (Non_DM), and to identify the molecular endotypes within PDR patients.Proteomic profiling of paired vitreous humor and plasma samples from 47 PDR patients, 26 Non_DR patients, and 48 Non_DM patients were conducted with Olink platform. The Olink platform includes 13 panels targeting 1161 proteins. Gene set enrichment analysis was applied for enriched pathways, and the K-means clustering method was used to identify different PDR clusters based on vitreous proteomic profiles.Proteomic analysis revealed significant differences in the vitreous humor of PDR patients compared to those in the Non_DR or Non_DM groups, with elevation of carbonic anhydrase (CA) family members as potential contributors to PDR pathophysiology. Plasma samples from PDR group exhibited less profound differences in proteomic profiles compared to the other two groups. Clustering analysis of PDR vitreous samples identified three distinct clusters as molecular endotypes of PDR patients. Of those, Cluster 3 was characterized by enrichment in CCL/CXCL/IL6/IL18 chemokines and pro-inflammatory signaling pathways, which may contribute to more severe PDR phenotypes.Significant differences in proteomic profiles were observed in PDR patients, especially in vitreous samples, with CA family members identified as potential therapeutic targets for PDR. Endotyping analysis of vitreous samples uncovered unique patient population with enriched CCL/CXCL/IL6/IL18 inflammatory pathways, highlighting the significance of local protein signature changes in PDR disease heterogeneity and its potential applications in patient stratification and therapeutic treatment
Corrigendum to "Biomimetic oral drug delivery: Translating nature's design into therapeutic innovation" [Colloids Surf. B: Biointerfaces 259 (2026) 115348].
No full textThe authors would like to add following disclaimer in the Declara
tion of competing Interest section:
necessarily reflect the official policy or position of Novartis or any of
its affiliates or officers.”
Disclaimer
“Among the authors, Muzaffaruddin Ahmed Madny states that the
views and opinions expressed in this article are his own and do not
DOI of original article: https://doi.org/10.1016/j.colsurfb.2025.11
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