Starvation induces metabolic hepatocyte reprogramming.

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Exposure-response analysis of asciminib efficacy and safety in patients with chronic myelogenous leukemia in chronic phase.

Objective This study aimed to evaluate the relationships between asciminib pharmacokinetics with efficacy in first line (1 L) and safety in 1 L and third line (3 L) patients with Philadelphia-positive chronic myeloid leukemia in chronic phase. Methods The key primary efficacy endpoint in ASC4FIRST (1 L, phase 3), the week-48 major molecular response (MMR), versus asciminib exposure relationship were evaluated using linear regression model; the same model was applied to assess safety endpoints vs. exposure in ASC4FIRST, ASCEMBL (3 L, phase 3) and first-in-human (3 L, phase 1) trials based on abnormal laboratory data, vital signs and adverse events. Results The probability of week-48 MMR in ASC4FIRST versus exposure (averaged daily AUC and Cmin) in 1 L was 70–80% for the second through fourth quartile AUC0−24 h and Cmin where majority of patients had relative dose intensity > 75%; the MMR rate in the first quartile exposure metrics was 45%. Of all safety endpoints in 1 L, the only statistically significant positive relationship with exposure was the worsening event for grade ≥ 3 lipase increase but event probabilities were low (5.8%-11.2%). Line of therapy was investigated as a covariate for all safety endpoints; newly diagnosed patients had a lower probability of all safety events occurring than in pretreated patients. Conclusion Treatment compliance optimizes the efficacy of asciminib in newly diagnosed patients. Safety incidences associated with asciminib were low. The 80 mg once-daily regimen provides an optimal benefit-risk profile in newly diagnosed patients. Similar efficacy is expected from the 40 mg twice-daily regimen in the same population

Pharmacotherapy of cardiovascular diseases from herbs and pills to nucleic acids A report from the European Society of Cardiology Cardiovascular Roundtable

Cardiovascular (CV) diseases continue to cause substantial morbidity and mortality. Risk factors are inadequately controlled, compliance with medi�cation remains suboptimal, and treatments are not sufficient to fully prevent the progression of atherosclerotic CV disease, heart failure, arrhyth�mias, and valvular heart diseases. An increased understanding of the genetic basis of CV diseases and advances in the technology of therapeutics have led to the development of nucleic acid–based therapies (NATs) for prevention and treatment of CV risk factors and diseases. Nucleic acid–based therapies can target disease pathways at the translational level preventing the formation of disease-causing proteins that could not be effectively targeted by other pharmacological therapeutics and will likely improve treatment adherence by providing long-acting effects over many months rather than daily treatment. These therapies include RNA-targeted therapeutics, gene editing therapeutics, and gene therapies. Challenges around the use of NATs may be unique with each new drug and new target and may include long-term unanticipated side effects, and issues around spe�cificity, targeting, and stability. Assessing NATs for marketing approval continues to pose challenges for regulatory agencies. These include their di�verse nature, limited data on pharmacology, clinical safety and efficacy, and the lack of long-term results. Barriers in clinical practice may include the lack of specific education, fear of off target effects, costs, and ethical challenges. Implementation of these novel therapies will require careful patient selection and education. Despite potentially high treatment costs, possible long-term cost savings could result from fewer healthcare visits due to infrequent NAT administrations, and lower rates of disease progression, hospitalization, and CV events due to sustained improvement in control of disease pathways and risk factors

The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development.

The use of artificial intelligence (AI) and machine learning (ML) in drug discovery has been well documented, but measures of levels of adoption, investments, and efficiencies gained from its use in clinical development have not yet been developed, captured or published. AI/ML use in clinical development is expected to increase, but its impact has not yet been systematically measured until now.The Tufts Center for the Study of Drug Development conducted a global online survey among pharmaceutical and biotechnology companies, contract research organizations (CROs), and data and technology vendors servicing drug developers. The survey gathered 302 responses assessing levels of AI/ML implementation across 36 distinct clinical trial planning and design, trial execution, and regulatory submission activities. The survey collected data on US dollar investment, time savings, and challenges and opportunities of AI/ML use in clinical development.Approximately one-third of the sample (36.9%) was not yet using or implementing AI/ML across 36 design and planning, execution, and regulatory submission activities; another 30.3% was beginning their AI/ML implementation (or piloting), 22.1% was partially implementing (or moving beyond pilots), and on average only 10.7% had fully implemented AI/ML (i.e., uses AI in most trials employing a repeatable process)

Double-stranded RNA induces retinal pigment epithelium cell degeneration and inflammation

RIG-I signaling has been previously implicated as a driver of inflammation to the retinal pigment epithelium (RPE) during age-related macular degeneration (AMD). Double-stranded RNA (dsRNA) is known to initiate RIG-I signaling and lead to a type I interferon response. We show through shRNA knockdown that RIG-I is essential for initiating an interferon response in iPS-RPE in response to both synthetic dsRNA-mimetic 3p-hpRNA and the double-stranded retrotransposable element Alu. Analysis of human tissue from patients suffering from AMD show accumulation of dsRNA, peaking at the geographic atrophy (GA) stage. Using a new murine model of 3p-hpRNA subretinal challenge to RPE cells, we confirmed that accumulation of dsRNA initiates a type I interferon response, as well as RPE and photoreceptor degeneration. Although RPE response to synthetic dsRNA was acute, extensive leukocyte migration was observed. The results from this study verify the importance of RIG-I signaling in regulating inflammation in the subretinal space and implicates dsRNA accumulation as a driver of AMD pathogenesis

Strain-release trifluoromethoxylation and pentafluorosulfanoxylation of [1.1.0]bicyclobutanes: expanded access to fluorinated cyclobutane hybrid bioisosteres.

Methods for formal bromo-trifluoromethoxylation and bromo-pentafluorosulfanoxylation of [1.1.0]bicyclobutanes using AgOCF3 or AgOSF5 and 1,3-dibromo-5,5-dimethylhydantoin are disclosed. These represent complementary strategies to the syntheses of SF5- and CF3SF4-containing cyclobutanes previously reported from our laboratory, ultimately enabling comparative structural studies and in vitro ADME profiling for various fluorinated cyclobutanes

ASKCOS: Open-Source, Data-Driven Synthesis Planning.

ConspectusThe advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. In this Account, we describe the range of data-driven methods and models that have been incorporated into the newest version of ASKCOS, an open-source software suite for synthesis planning that we have been developing since 2016. This ongoing effort has been driven by the importance of bridging the gap between research and development, making research advances available through a freely available practical tool. ASKCOS integrates modules for retrosynthetic planning, modules for complementary capabilities of condition prediction and reaction product prediction, and several supplementary modules and utilities with various roles in synthesis planning. For retrosynthetic planning, we have developed an Interactive Path Planner (IPP) for user-guided search as well as a Tree Builder for automatic planning with two well-known tree search algorithms, Monte Carlo Tree Search (MCTS) and Retro*. Four one-step retrosynthesis models covering template-based and template-free strategies form the basis of retrosynthetic predictions and can be used simultaneously to combine their advantages and propose diverse suggestions. Strategies for assessing the feasibility of proposed reaction steps and evaluating the full pathways are built on top of several pioneering efforts that we have made in the subtasks of reaction condition recommendation, pathway scoring and clustering, and the prediction of reaction outcomes including the major product, impurities, site selectivity, and regioselectivity. In addition, we have also developed auxiliary capabilities in ASKCOS based on our past and ongoing work for solubility prediction and quantum mechanical descriptor prediction, which can provide more insight into the suitability of proposed reaction solvents or the hypothetical selectivity of desired transformations. For each of these capabilities, we highlight its relevance in the context of synthesis planning and present a comprehensive overview of how it is built on top of not only our work but also of other recent advancements in the field. We also describe in detail how chemists can easily interact with these capabilities via user-friendly interfaces. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks by complementing expert decision making and route ideation. It is our belief that CASP tools are an important part of modern chemistry research and offer ever-increasing utility and accessibility

Predictive Stability in Biopharmaceuticals and Vaccines: Perspectives and Recommendations towards Accelerating Patient Access

Industry position paper elucidating a 360-degree cross-functional and cross-company perspective on predictive stability approaches for large molecules with intent to influence the field and health authorities

Predicting and Confirming Bioequivalence of Alpelisib Oral Granules and Tablets for Patients With PIK3CA-Related Disorders.

Alpelisib, an oral α-specific phosphoinositide 3-kinase (PI3K) inhibitor, has been shown to be safe and effective for some patients with gain-of-function mutation in the PIK3CA oncogene. Alpelisib has received US FDA accelerated approval as Vijoice® film-coated tablets to treat severe PIK3CA-Related Overgrowth Spectrum (PROS). PROS typically displays clinical manifestations in the first year of patient life. Therefore, oral granules were developed as an age-appropriate pediatric dosage form. Bioequivalence between alpelisib granules and tablet and the effect of food on granules pharmacokinetics were assessed in a single-center, randomized, three-treatment, six-sequence, three-period, crossover study among 60 healthy adults. Participants were randomly assigned to receive a single 50-mg alpelisib dose as: (i) tablet following a meal, (ii) granules following a meal, and (iii) granules while fasting. Statistical analysis of non-compartmental pharmacokinetic parameters demonstrated bioequivalence between the 50-mg alpelisib granules and tablet forms when administered with food: estimated geometric mean ratios (90% confidence interval) for granules-versus-tablet area under the curve (AUC) from time zero to infinity (AUCinf), to the last measurable concentration (AUClast) and maximum observed concentration (Cmax) were 0.984 (0.952, 1.02), 0.980 (0.946, 1.02), and 0.947 (0.891, 1.01), respectively. No clinically relevant food effect on 50-mg alpelisib granules pharmacokinetics was observed. These results were accurately predicted using physiologically based biopharmaceutical modeling. Alpelisib granules provide a bioequivalent alternative to tablets for patients prescribed a 50-mg dose and have difficulty swallowing tablets, an important consideration for convenience and compliance of this standard-of-care chronic therapy for patients with PROS. This study was registered in ClinicalTrials.gov on January 4, 2022 (NCT05195892)

Multiparametric grading of glaucoma severity by histopathology can enable post-mortem substratification of disease state.

Neurodegeneration in glaucoma patients is clinically identified through longitudinal assessment of structure-function changes, including intraocular pressure, cup-to-disc ratios from fundus images, and optical coherence tomography imaging of the retinal nerve fiber layer. Use of human post-mortem ocular tissue for basic research is rising in the glaucoma field, yet there are challenges in assessing disease stage and severity, since tissue donations with informed consent are often unaccompanied by detailed pre-mortem clinical information. Further, the interpretation of disease severity based solely on anatomical and morphological assessments by histology can be affected by differences in death-to-preservation time and tissue processing. These are difficult confounders that cannot be easily controlled. As pathogenesis and molecular mechanisms can vary depending on the stage and severity of glaucoma, there is a need for the field to maximize use of donated tissue to better understand the molecular mechanisms of glaucoma and develop new therapeutic hypotheses. Further, there is a lack of consensus around the molecular RNA and protein markers that can be used to classify glaucoma severity. Here, we describe a multiparametric grading system that combines structural measurements of the retinal nerve fiber layer with linear regression and principal component analyses of molecular markers of retinal ganglion cells and glia (RBPMS, NEFL, IBA1 and GFAP) to stratify post-mortem glaucoma eyes by the severity of disease. Our findings show that a quantitative grading approach can stratify post-mortem glaucoma samples with minimal clinical histories into at least three severity groups and suggest that this type of approach may be useful for researchers aiming to maximize insights derived from eye bank donor tissue