7196 research outputs found
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Exercise-Induced Myostimulin Enhances Muscle Function in Health and Disease
No full textMusculoskeletal diseases are a leading contributor to years lived with disability worldwide. While exercise offers significant benefits for people with these conditions, many individuals do not engage in adequate physical activity. Consequently, there is growing interest in pharmacological interventions that can emulate essential health-promoting effects of exercise. By integrating transcriptomics data of exercised skeletal muscle, we identified C1orf54/C1ORF54 as a novel exercise-responsive gene in mice and humans. We demonstrate that removal of the first sixteen N-terminal amino acids of C1ORF54 gives rise to a previously uncharacterized protein that stimulates the proliferation of muscle precursor cells and which we named myostimulin. Intriguingly, repeated intermittent treatment of mice with recombinant myostimulin boosts maximal isometric strength in mice within a week. Moreover, we have engineered a variant with improved biophysical properties, increased biological activity in vitro and enhanced efficacy in vivo. This variant even accelerates the recovery of muscle strength from axonotmesis, a condition associated with pronounced muscle weakness. Our data ascribe to myostimulin a role for enhancing the regenerative capacity of skeletal muscle and mediating functional adaptations characteristic of sustained resistance training. Therefore, myostimulin could be an innovative, fast acting therapeutic for certain human musculoskeletal diseases, injuries and other disorders that improve with exercise
Analysis of retrospective natural history data collected from patients with SYNGAP1-related disorders: a preliminary examination of the Ciitizen database
No full textBackground: SYNGAP1-related disorder (SRD) is a rare neurodevelopmental disorder caused by genetic mutations and variants. One major challenge is the characterization of the SRD, which requires assessment of several outcomes. We considered natural history data from the Ciitizen database on 65 patients with SRD. Eight data domains have been explored: demographics, genetics, growth parameters, standardized clinical scales, developmental skills, neurological examinations, hospitalizations, and seizures. Exploratory analysis tools such as visualization, summary statistics, and non-parametric statistical modeling were utilized.
Results: Age at SRD diagnosis (median [IQR]=3 [2, 5] years; [min, max]=[1, 17] years) was similar by sex. No evidence of a high frequency allele in SYNGAP1 was found, indicating no dominant mutation in this patient population. Growth parameters of SRD children appeared normal across height, weight, and head circumference. Developmental data was indicative of delayed development and language reversion. Standardized assessment data were largely sparse. Neurological exam data demonstrated ataxia and muscle tone issues. Hospitalization data highlighted substantial healthcare burden, largely due to seizures. Absence, atonic, and myoclonic where the most common types of seizures.
Conclusion: Ciitizen data provides important insights into the natural course of SRD. This information can provide utility in clinical practice and inform the design of clinical trials in SRD. Limitations to our analysis include sparsity of standardized clinical scales data, crude statistical methodology, and bias induced by patients with older ages of diagnoses
Long-Term Safety Evaluations in the Presence of Switching: Evaluation of Two Approaches.
No full textEvaluating the long-term safety of approved drugs for chronic indications is essential. This assessment ensures that the benefits continue to outweigh the risks beyond the follow-up observed in the clinical trials supporting market authorization. Consequently, these evaluations are mandated or recommended by multiple regulatory agencies and necessitate collecting and analyzing data from longitudinal cohorts in real-world settings post-market authorization. One challenge in analyzing and interpreting results using these sources is the complexity of long-term real-world drug utilization patterns in a competitive landscape, including switching between multiple drugs. Several methods have been developed to evaluate comparative long-term safety under real-world conditions. These methods include the experimental hierarchical approach, which extends the analytical follow-up period for the test drug to include the time after switching away, and the overlapping approach, which extends the follow-up period for both the test and comparator drugs to include the time after switching away. This paper uses multistate model methodology to consider initial and subsequent exposures in evaluating the estimators in these two methods. Our mathematical evaluations and simulations demonstrate that the estimators inflate the type-1-error across different switching and outcome incidence rate scenarios. Therefore, we propose a minor modification of the estimators to preserve the type-1-error. Currently used methods are simple but biased and lack clearly defined estimands. Methods based on multistate models may help identify and refine new estimands for evaluating long-term safety
Automation of Multistep Reaction-Based Solid-Phase Synthesis Using Novel Polystyrene-Coated Magnetic Particles.
No full textWe have developed novel types of polystyrene-coated magnetic particles for versatile applications in multistep reaction-based solid-phase organic synthesis. These particles feature a protective polymer shell that allows them to function under various reaction conditions while maintaining the beneficial properties of traditional polystyrene beads, such as swelling and shrinking in organic solvents. We applied the particles in diverse multistep syntheses, including and combining amide couplings, nucleophilic aromatic substitutions, and Suzuki-Miyaura couplings at tens of micromole scale. The ferrimagnetic property of the particles enables their reversible immobilization using an external magnetic field, facilitating efficient reagent change cycles through simple aspiration of the separated supernatant liquid. This immobilization principle was applied to particle conditioning, reactions, washing, deprotection, and linker cleavage, allowing for fully automated parallel syntheses in microtiter plates using an automated liquid-handling system. This platform has already become routine for hit syntheses from various high-throughput screening campaigns and will enable the efficient production of a wide variety of new compounds by solid-phase synthesis. Ongoing developments on magnetic particles, reaction conditions, and automated processing will further extend the application range in medicinal chemistry and beyond
Myeloid cells mediate interferon-driven resistance to immunotherapy in advanced renal cell carcinoma.
No full textSustained type-I and type-II interferon (IFN) signaling can drive multiple mechanisms of resistance to immune checkpoint blockade (ICB). Here, we used single-cell RNA sequencing data to characterize the effects of IFNs in the tumor-immune microenvironment (TME) of renal cell carcinoma (RCC) and then examined how IFN-driven cellular phenotypes modulate ICB efficacy. Using mixed-effects models, we inferred the IFN inducibility of putative IFN-stimulated genes (ISGs) within cell types. Genes encoding inhibitory ligands and immune checkpoints were strongly expressed and IFN inducible in macrophages but less so in RCC tumor cells. In orthogonal clinical trial cohorts, a signature of myeloid IFNγ signaling, but not tumor IFNγ signaling, predicted primary resistance to first-line ICB plus anti-VEGF therapy. Functionally, IFNγ-conditioned macrophages inhibited T cell killing of RCC tumor cells in vitro. Our inferential modeling approach offers a framework for biomarker discovery through deconvolution of cytokine signaling effects in the TME and points to myeloid cells as mediators of tumor-extrinsic, IFN-driven resistance to immunotherapy in RCC
Evaluating Foundation Models for In-Silico Perturbation
No full textIn-silico perturbation (ISP) offers a scalable alternative to traditional gene perturbation experiments, yet evaluation of foundation models for ISP remains underexplored. We introduce a novel evaluation framework, the single cell in-silico perturbation framework (scISP), to benchmark ISP models against in-vitro experimental data using biologically meaningful metrics, including cell state separation accuracy, ISP accuracy, and mean reciprocal rank (MRR) for predicting perturbed genes. Complementary functional analyses evaluate model performance across diverse gene categories. Using scISP, we assess two well-known pre-trained foundation models, Geneformer and scGPT, alongside the deep learning model, GEARS, highlighting their respective strengths and limitations in simulating cell state transitions and identifying perturbed genes. These analyses reveal intrinsic differences across models, offering opportunities to optimize foundation models for specific biological contexts or gene categories. Our extensible framework establishes a robust bridge between computational predictions and experimental validation, advancing gene perturbation research and biological discovery
A pharmacometrics-informed trial simulation framework for optimizing study designs for disease-modifying treatments in rare neurological disorders
No full textThe development of new treatments for rare neurological diseases (RNDs) may be very challenging due to limited natural history data, lack of relevant biomarkers and clinical endpoints, small and heterogeneous patient populations and other complexities. A systematic approach is needed for comparing various design and analysis strategies to identify “optimal” approaches for a clinical trial in a chosen RND with the given resource constraints. For this purpose, we propose a pharmacometrics-informed clinical scenario evaluation framework (CSE-PMx), which includes some important research hallmarks relevant to RND clinical trials: a disease progression model for simulating individual longitudinal outcomes, the choice of a suitable randomization method for trial design, and an option to perform subsequent statistical analysis with randomization tests. We illustrate the utility of CSE-PMx for an exemplary randomized trial to compare the disease-modifying effect of an experimental treatment versus control in patients with Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). In the considered example, our simulation evidence suggests that a nonlinear mixed-effects model (NLMEM) with a population-based likelihood ratio test analysis is valid, robust, and more powerful than some conventional methods such as two-sample t-test or analysis of covariance (ANCOVA). Our proposed framework is very flexible and generalizable to clinical research in other rare disease indications
Reality Check: The Aspirations of the European Health Data Space Amidst Challenges in Decentralized Data Analysis
No full textThe European Health Data Space (EHDS) aspires to enable secure, interoperable, and decentralized health data usage across Europe. This paper explores legal and technical challenges in implementing EHDS goals, particularly for secondary data use. It highlights federated and swarm learning as promising yet complex solutions, requiring robust infrastructure, standardization, and regulatory clarity. We emphasize the need for coordinated legislative and technological advances to realize EHDS ambitions
Bench-stable reagents for modular access to persulfuranyl scaffolds
No full textThe vast expanse of chemical space offers limitless possibilities for medicinal chemists, particularly in discovering novel scaffolds and functional groups with drug-like properties. However, the widespread adoption of these functional groups is often initially limited by their synthetic accessibility and functionalization. Among these functional groups, the pentafluorosulfanyl group is long considered a possible (bio)isostere for tert-butyl and trifluoromethyl groups, but is synthetically challenging to access due to limited chemical reagents and methodologies. To bridge this gap, we have developed a general pentafluorosulfanylation platform that employs bench-stable solid reagents to generate SF₅ radicals via a decarboxylation and β-scission sequence. This strategy enables a variety of operationally simple transformations, expanding the accessibility of SF₅-containing molecules. Notably, this reagent design is also adaptable to other persulfuranyl groups, such as trifluoromethyl tetrafluorosulfanyl and aryl tetrafluorosulfanyl groups. Taken together, generating an armamentarium of these stable reagents and practitioner-friendly chemical methodologies will enable the synthesis of challenging and biologically relevant sulfur(VI) chemical scaffolds in an expedient manner
Leveraging Model-Informed Drug Development to Predict Asciminib Efficacy in Second-Line Treatment of Chronic Myeloid Leukemia in Chronic Phase
No full textBackground and objectives
The efficacy of asciminib was proven in newly diagnosed (first-line, 1L) patients with Philadelphia-positive chronic myeloid leukemia in chronic phase (Ph+ CML-CP) and in patients treated with at least two prior tyrosine kinase inhibitors (third-line, 3L+). Given that no randomized controlled trial has been conducted for second-line (2L) patients with CML, this analysis aims to infer the efficacy of asciminib in the 2L setting using the 80 mg once-daily dosing regimen and to support the use of asciminib in patients with CML-CP irrespective of line of therapy.
Methods
Data (n = 430) were used from three studies, including first-in-human and ASCEMBL in 3L+ and ASC4FIRST in 1L trials, to evaluate the effect of line of therapy on efficacy on the basis of the time-course of BCR::ABL1 mRNA transcripts. Previously adapted to characterize the effect of nilotinib as a 1L and 2L therapy on a CML surrogate marker, the model has three compartments representing quiescent leukemic stem cells and proliferating drug-susceptible and resistant bone marrow cells, wherein the effect of asciminib was modeled as an enhancement of the elimination of susceptible cells.
Results
Asciminib efficacy in 2L was inferred from 1L by borrowing information from nilotinib data. A credibility assessment showed robust prediction accuracy and precision of the model with external 2L data from the ASC2ESCALATE study (n = 36). Major molecular response (MMR) rates in 2L were predicted to be 61–67% at week 48 and 70–76% at week 96, with the 80 mg total daily dose.
Conclusions
A model-informed drug development (MIDD) approach was applied to predict 2L efficacy and supported global regulatory approval of asciminib across treatment lines, despite limited clinical data in 2L