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Foundational Principles for the Quantitative Translation of T-Cell Therapeutics for Hematologic Malignancies and Immunology.
No full textT-cell engaging antibodies (TCEs) and chimeric antigen receptor (CAR) T cells (CAR-T cells) are among precision medicine therapies that have revolutionized the treatment of hematologic cancers. Their success in oncology has piqued interest in translating this promise into additional indications, such as autoimmune disorders. This review discusses the foundational principles for mechanistic modeling to provide a unified assessment framework for cross-modality (i.e., CAR-T cells vs. TCEs) and cross-indication (i.e., oncology vs. immunology) translation. This framework captures the unique elements of each modality, such as CAR-T cellular kinetics, TCE pharmacokinetics, and complex formation with target cells, as well as shared elements such as B-cell kinetics and biodistribution across indications. We describe how this integrated approach can lead to informed decision making for more personalized and effective treatment strategies with these immune therapies
Fingolimod as a Potential Cerebroprotectant- results from the Stroke Preclinical Assessment Network
No full textABSTRACT
46 Background:
47 Fingolimod is an immunomodulatory drug used for relapsing Multiple Sclerosis that has
48 shown promising effects in stroke treatment, including improvements in neurofunctional
49 recovery and a reduction in infarct size. Fingolimod modulates the sphingosine-1-
50 phosphate (S1P) receptors, which leads to the internalization of S1P receptors on T and
51 B lymphocytes, thereby attenuating their immune response. Here, we report a secondary
52 analysis from the Stroke Preclinical Assessment Network, a multi-laboratory preclinical
53 trial. We assessed the effects of fingolimod versus vehicle on stroke outcomes to better
54 evaluate its therapeutic potential
55 Methods:
56 The animal population (n = 409) comprised male and female animals treated with
57 fingolimod or vehicle. We used 4 clinically relevant models: young healthy mice (10-12
58 weeks-old (w.o)), aging mice (16+/-1 month-old), obesity induced-hyperglycemic mice
59 (OIH) fed with a high-fat diet for 12 weeks (16 w.o) and spontaneously hypertensive rats
60 (16 +/- 1 w.o). Stroke was induced by the middle cerebral artery occlusion (MCAO) for
61 one hour, followed by reperfusion. Animals received a total of six intraperitoneal injections
62 of 0.5 mg/kg of fingolimod or vehicle. Functional outcomes on turning preference in the
63 corner test and foot-faults while walking on a grid were measured at days 7 and 28. Lesion
64 size and brain morphometry were evaluated at day 2 and day 30 by MRI.
65 Results:
66 Overall, fingolimod did not improve morphological and functional outcomes. However,
67 fingolimod effects varied depending on sex or the comorbidity model. Fingolimod
68 promoted a better outcome in the corner test in aging females. In contrast, it favored a
69 worse outcome in obesity-induced hyperglycemic mice at day 7. Despite having no effect
70 on survival rates or lesion size, fingolimod attenuated the midline retraction at day 30 in
71 aging males, consistent with less atrophy.
72 Conclusion:
73 While fingolimod did not significantly benefit the overall primary functional outcome, its
74 effects varied with sex and comorbidity models, underscoring how the therapeutic
75 potential of a particular drug can differ in a heterogenous population
Letter to the editor of the Brit J Clin Pharmacol on Heuberger et al. 2025
No full textNo abstract as this is a letter. This is the entire letter:
Dear editor,
Heuberger and colleagues [1] need to be commended for their effort to systematize approaches to sentinel dosing in early phase clinical pharmacology trials. To not only consider first-in-human studies for these questions but also reflecting on situations where early clinical trials move into not yet investigated exposures or duration of exposures expands the concept of sentinel dosing and is a very important thought overall. Bonate et al. on behalf of the ACCP Public Policy Committee have recently advocated for a risk-based approach for decisions on sentinel dosing [2]. Based on the two most recent misadventures (TGN1412 and BIA10-2474) and their learnings, we feel there are two important additional considerations: A) a novel mode of action with antagonistic properties has a more predictable pre-defined maximum effect than an agonist and the potential risk to trial participants may therefore be easier to assess. B) the likelihood of unique human metabolites (as discussed for BIA10-2474) which are not seen under single dose conditions and accumulate eliciting toxicities under multiple dosing conditions is an important aspect to take into account. We therefore believe that these two aspects which are not explicitly mentioned in [1] should be considered when assessing the overall risk and included in the framework in assessing the need for sentinel dosing in early clinical trials
Adaptive off-policy inference for M-estimators under model misspecification
No full textWhen data are collected adaptively, such as in bandit algorithms, classical statistical approaches such as ordinary least squares and M-estimation will often fail to achieve asymptotic normality. Although recent lines of work have modified the classical approaches to ensure valid inference on adaptively collected data, most of these works assume that the model is correctly specified. We propose a method that provides valid inference for M-estimators that use adaptively collected bandit data with a (possibly) misspecified working model. A key ingredient in our approach is the use of flexible machine learning approaches to stabilize the variance induced by adaptive data collection. A major novelty is that our procedure enables the construction of valid confidence sets even in settings where treatment policies are unstable and non-converging, such as when there is no unique optimal arm and standard bandit algorithms are used. Empirical results on semi-synthetic datasets constructed from the Osteoarthritis Initiative demonstrate that the method maintains type I error control, while existing methods for inference in adaptive settings do not cover in the misspecified case
Discovery of NP3-742: A Structurally Diverse NLRP3 Inhibitor Identified through an Unusual Phenol Replacement
No full textNLRP3 is a molecular sensor present in innate immune cells which recognizes a variety of danger signals such as MSU, ATP, or Aβ. Upon activation, it seeds a protein complex termed the inflammasome, which leads to secretion of the proinflammatory cytokines IL-1β and IL-18 and initiates pyroptotic cell death. NLRP3 inflammasome activation has been associated with a wide range of diseases including atherosclerosis, gout, and cancer. In this publication, we describe the replacement of the phenol moiety with indoles in the recently described pyridazine scaffold. This replacement required a shift of the hydrogen bond donor from the “ortho” to the “meta” position, relative to the pyridazine ring. Initial indole analog 7 demonstrated a robust in vivo IL-1β inhibition, but also a significant hERG inhibition. Decreasing lipophilicity led to the discovery of NP3-742, demonstrating a favorable overall profile including diminished hERG inhibition and in vivo efficacy in a mouse peritonitis model
Considerations for Microbiological Control Strategy during Oligonucleotide Drug Substance Manufacturing
No full textEnsuring the quality and safety of synthetic oligonucleotide drug substances demands stringent microbial contamination control. While chemical synthesis inherently carries a lower risk compared to biological manufacturing, robust controls remain critical to minimize potential microbial proliferation at specific stages of the process. Given the limited regulatory guidance directly addressing oligonucleotides, effective contamination control strategies must be built upon thorough risk assessments and established best practices.
This paper, drawing on the collective expertise of the European Pharma Oligonucleotide Consortium (EPOC), provides comprehensive recommendations for microbiological control in oligonucleotide manufacturing. Key points include facility design, environmental monitoring, equipment cleaning, in-process controls, and analytical methods. A thorough risk assessment and a holistic approach to microbial management are advocated. Detailed methodologies for risk evaluation, mitigation, and acceptance of residual risks are outlined. This strategic framework aims to proactively manage potential microbiological hazards, ensuring the consistent production of high-quality oligonucleotide therapeutics
TM Academy Brochure 2025
No full textThis TM Academy Brochure to be used for the recruitment of TM Academy fellow
A cooperative release of mitochondrial DNA from platelets and neutrophils drives an interferon signature in systemic sclerosis.
No full textMitochondria are organelles with a hypomethylated circular genome. Mitochondrial DNA (mtDNA) in the systemic circulation has been implicated in inflammation. This study investigates the role of circulating DNA in systemic sclerosis (SSc) and the cellular mechanisms governing its release.Total DNA was isolated from plasma of healthy individuals and SSc patients. Copy numbers were analyzed for mtDNA (ATP-6) and GAPDH abundance by qPCR. mtDNA was isolated from HC and SSc patients. Neutrophils and platelets were incubated with SSc patients' plasma and mtDNA, and NET formation was assessed by SytoxGreen and immunostainings. Platelets were tested for mtDNA release propensity. DNA oxidation was evaluated by MitoSOX Red staining in vitro and 8-OHdG ELISA of patient plasma. Plasma IFN type 1 and CXCL4 were measured by ELISA. IFN signaling activation capacity was evaluated utilizing THP1 reporter cells and confirmed by a whole blood bulk RNA transcriptomic analysis.Median plasma mtDNA levels were 152-fold higher in SSc patients compared to healthy controls (HC), while nDNA levels were similar. mtDNA from SSc plasma was highly oxidized. SSc-derived mtDNA efficiently promoted its own release by NETosis, most potently in SSc patient neutrophils, and by platelet activation. Oxidized mtDNA from SSc platelets in complex with CXCL4 further stimulated mtDNA release in both neutrophils and platelets. mtDNA plasma concentrations correlated with type I IFN concentrations in SSc patient blood, and SSc blood exhibited elevated interferon-stimulated gene (ISG) expression. SSc plasma-derived mtDNA induced IFN signaling and NET formation via endosomal TLR, cGAS/STING and the JAK/STAT pathway. The type I IFN pathway further promoted NETosis and mtDNA release since IFN receptor (IFNAR) and Janus kinase (JAK) inhibition antagonized the proNETotic effects of IFN.SSc plasma is characterized by highly abundant mtDNA, which drives feedback loops amplifying its own release from both neutrophils and platelets. Thus, mtDNA contributes to inflammation and tissue damage in SSc
Assembly of α-Aryl Sulfones and Allyl Sulfones and Their Amide Analogues via Copper-Catalyzed Coupling Reactions.
No full textA method is described for the synthesis of α-aryl and allylic sulfones and sulfonamides through CuBr/oxalamide-catalyzed coupling at 40 °C. This transformation involves coupling with (hetero)aryl or vinyl bromides and 2-substituted sulfonylacetates (or amide analogues), proceeding via spontaneous hydrolysis and decarboxylation. The conditions are compatible with various functional groups and heterocycles, allowing for the diverse syntheses of the target compounds
Prediction of aggregation in monoclonal antibodies from molecular surface curvature
No full textProtein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the aggregation of monoclonal antibodies in solution using machine learning methods. These efforts have yielded varying levels of success, with current state-of-the-art AI approaches achieving good prediction accuracies (r=0.86). Here, we demonstrate the prediction of aggregation rate in monoclonal antibodies with beyond state-of-the-art reliability using a coupled AI-MD-Molecular surface curvature modelling platform. The scientific novelty of this approach lies in using local geometrical surface curvature of proteins as the core element for protein stability analysis. By combining local surface curvature and hydrophobicity, as derived from time-dependent MD simulations, we are able to construct aggregation predictive features that, when coupled with linear regression machine learning techniques, give a high prediction accuracy (r=0.91) on a dataset of 20 molecules. More generally, this approach shows significant potential for quantitative in silico screening and prediction of protein aggregation, which is of great scientific and industrial relevance, particularly in biopharmaceutics