Orally Bioavailable Macrocyclic Peptide That Inhibits Binding of PCSK9 to the Low Density Lipoprotein Receptor

Circulation, Год журнала: 2023, Номер 148(2), С. 144 - 158

Опубликована: Май 1, 2023

Background: Inhibition of PCSK9 (proprotein convertase subtilisin/kexin type 9)-low density lipoprotein receptor interaction with injectable monoclonal antibodies or small interfering RNA lowers plasma low lipoprotein-cholesterol, but despite nearly 2 decades effort, an oral inhibitor is not available. Macrocyclic peptides represent a novel approach to target proteins traditionally considered intractable small-molecule drug design. Methods: Novel mRNA display screening technology was used identify lead chemical matter, which then optimized by applying structure-based design enabled synthetic chemistry macrocyclic peptide (MK-0616) exquisite potency and selectivity for PCSK9. Following completion nonclinical safety studies, MK-0616 administered healthy adult participants in single rising-dose Phase 1 clinical trial designed evaluate its safety, pharmacokinetics, pharmacodynamics. In multiple-dose taking statins, once daily 14 days characterize the pharmacodynamics (change cholesterol). Results: displayed high affinity ( K i = 5pM) vitro sufficient bioavailability preclinically enable advancement into clinic. studies adults, doses were associated >93% geometric mean reduction (95% CI, 84–103) free, unbound PCSK9; on statin therapy, multiple–oral-dose regimens provided maximum 61% 43–85) cholesterol from baseline after once-daily dosing 20 mg MK-0616. Conclusions: This work validates use identification therapeutic agents, exemplified inhibitor, has potential be highly effective lowering therapy patients need.

Язык: Английский

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AlphaFold2 protein structure prediction: Implications for drug discovery

Current Opinion in Structural Biology, Год журнала: 2023, Номер 78, С. 102526 - 102526

Опубликована: Янв. 6, 2023

Язык: Английский

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Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine

Chinese Medicine, Год журнала: 2023, Номер 18(1)

Опубликована: Ноя. 8, 2023

Abstract Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at level biological targets and pathways. The effective study traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, integrative efficacy, perfectly corresponds to application network pharmacology. Currently, has been widely utilized clarify physiological activity TCM. In this review, we comprehensively summarize in TCM reveal its potential verifying phenotype underlying causes diseases, realizing personalized accurate We searched literature using “TCM pharmacology” “network as keywords from Web Science, PubMed, Google Scholar, well National Knowledge Infrastructure last decade. origins, development, are closely correlated with which applied China thousands years. have same core idea promote each other. A well-defined research strategy several aspects research, including elucidation basis syndromes, prediction targets, screening active compounds, decipherment mechanisms diseases. However, factors limit application, such selection databases algorithms, unstable quality results, lack standardization. This review aims provide references ideas encourage precise use medicine.

Язык: Английский

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Emerging trends in organ-on-a-chip systems for drug screening

Acta Pharmaceutica Sinica B, Год журнала: 2023, Номер 13(6), С. 2483 - 2509

Опубликована: Фев. 15, 2023

New drug discovery is under growing pressure to satisfy the demand from a wide range of domains, especially pharmaceutical industry and healthcare services. Assessment efficacy safety prior human clinical trials crucial part development, which deserves greater emphasis reduce cost time in discovery. Recent advances microfabrication tissue engineering have given rise organ-on-a-chip, an

Язык: Английский

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Revolutionizing drug formulation development: The increasing impact of machine learning

Advanced Drug Delivery Reviews, Год журнала: 2023, Номер 202, С. 115108 - 115108

Опубликована: Сен. 27, 2023

Язык: Английский

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Self-Driving Laboratories for Chemistry and Materials Science

Chemical Reviews, Год журнала: 2024, Номер 124(16), С. 9633 - 9732

Опубликована: Авг. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Язык: Английский

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Health Digital Twins in Life Science and Health Care Innovation

The Annual Review of Pharmacology and Toxicology, Год журнала: 2023, Номер 64(1), С. 159 - 170

Опубликована: Авг. 10, 2023

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs improve discovery development by providing a data-driven approach inform target selection, delivery, design clinical trials. also offer new applications into precision therapies decision making. The deployment at scale could bring public health monitoring intervention. Next steps include challenges such as addressing socioeconomic barriers ensuring the representativeness technology based on training validation data sets. Governance regulation HDT still in early stages.

Язык: Английский

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Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

Nucleic Acids Research, Год журнала: 2024, Номер 52(W1), С. W469 - W475

Опубликована: Апрель 18, 2024

Abstract Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate drugs human body, are described from four perspectives: absorption, distribution, metabolism excretion—all closely related to fifth perspective, toxicity (ADMET). Since obtaining ADMET data vitro, vivo or pre-clinical stages time consuming expensive, many efforts have been made predict via computational approaches. However, majority available methods limited their ability provide pharmacokinetics for diverse targets, ensure good overall accuracy, offer ease use, interpretability extensibility further optimizations. Here, we introduce Deep-PK, deep learning-based prediction, analysis optimization platform. We applied graph neural networks graph-based signatures as graph-level yield best predictive performance across 73 endpoints, including 64 9 general properties. With these powerful models, Deep-PK supports molecular interpretation, aiding users optimizing understanding given input molecules. The freely at https://biosig.lab.uq.edu.au/deeppk/.

Язык: Английский

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Machine learning in preclinical drug discovery

Nature Chemical Biology, Год журнала: 2024, Номер 20(8), С. 960 - 973

Опубликована: Июль 19, 2024

Язык: Английский

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Cancer metabolism and carcinogenesis

Experimental Hematology and Oncology, Год журнала: 2024, Номер 13(1)

Опубликована: Янв. 29, 2024

Abstract Metabolic reprogramming is an emerging hallmark of cancer cells, enabling them to meet increased nutrient and energy demands while withstanding the challenging microenvironment. Cancer cells can switch their metabolic pathways, allowing adapt different microenvironments therapeutic interventions. This refers heterogeneity, in which cell populations use pathways sustain survival proliferation impact response conventional therapies. Thus, targeting heterogeneity represents innovative avenue with potential overcome treatment resistance improve outcomes. review discusses patterns developmental stages, summarizes molecular mechanisms involved intricate interactions within metabolism, highlights clinical vulnerabilities as a promising regimen. We aim unravel complex characteristics develop personalized approaches address distinct traits, ultimately enhancing patient

Язык: Английский

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