Intelligent Pharmacy最新文献_第3页

Stratification of cephalosporins based on physicochemical and pharmacokinetic variables using multivariate statistical tools 基于物理化学和药代动力学变量使用多变量统计工具的头孢菌素分层

Intelligent Pharmacy

Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2024.09.004

Carlos Alberto Escobar Angulo, Antistio Alviz Amador, Julián Javier Martínez Zambrano

Introduction

Cephalosporins, a class of beta-lactam antibiotics, are commonly used in medical practice. However, their potential advantages, based on physicochemical and pharmacokinetic variables, are often overlooked. This research, proposing strategies based on multivariate statistics to stratify different cephalosporins, is a significant step towards providing the prescribing team with more rational and effective options. The potential benefits of this research are promising, as it has the potential to significantly improve the efficacy and safety of cephalosporin therapy.

Method

Exploratory study and review of pharmacokinetic parameters of cephalosporins. Data were extracted from DrugBank go.drugbank.com, and multivariate statistical techniques such as Pearson correlation and cluster analysis were applied. This approach allowed the identification of groupings of cephalosporins with similar characteristics, thus facilitating their rational selection in clinical practice.

Results

The results reveal that cefazolin, cefotetan, cefoperazone, and ceftriaxone form the conglomerate with the most favorable properties for reaching effective concentrations at the site of action due to their high solubility, high percentage of binding to plasma proteins, and adequate residence times in the organism. Solubility, protein binding, half-life, MRT, molecular weight, volume of distribution, number of interactions, and pKa are all critical factors that influence the efficacy and safety of cephalosporin therapy.

Conclusions

It is relevant to highlight the use of multivariate statistics as a tool for drug selection and rational use. In the present study, cefazolin, cefotetan, cefoperazone, and ceftriaxone were highlighted as the best therapeutic alternatives according to the variables selected for the study.

头孢菌素是一类β -内酰胺类抗生素，在医疗实践中广泛使用。然而，基于物理化学和药代动力学变量，它们的潜在优势往往被忽视。本研究提出基于多元统计对不同头孢菌素进行分层的策略，为处方团队提供更合理有效的选择迈出了重要的一步。这项研究的潜在益处是有希望的，因为它有可能显著提高头孢菌素治疗的疗效和安全性。方法对头孢菌素类药物的药动学参数进行探索性研究和综述。数据提取自DrugBank go.drugbank.com，采用Pearson相关、聚类分析等多元统计技术。这种方法可以识别具有相似特征的头孢菌素的分组，从而促进临床实践中合理选择头孢菌素。结果头孢唑林、头孢替坦、头孢哌酮和头孢曲松形成的复合物质具有较高的溶解度、与血浆蛋白的结合率高、在体内停留时间长等特点，最有利于在作用部位达到有效浓度。溶解度、蛋白结合、半衰期、MRT、分子量、分布体积、相互作用次数和pKa都是影响头孢菌素治疗疗效和安全性的关键因素。结论强调多变量统计作为药物选择和合理使用的工具具有重要意义。在本研究中，根据研究选择的变量，头孢唑林、头孢替坦、头孢哌酮和头孢曲松被强调为最佳治疗方案。

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引用次数: 0

Machine learning & deep learning tools in pharmaceutical sciences: A comprehensive review 制药科学中的机器学习和深度学习工具：综合综述

Intelligent Pharmacy

Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2024.11.003

Saleem Javid , Abdul Rahmanulla , Mohammed Gulzar Ahmed , Rokeya sultana , B.R. Prashantha Kumar

Drug discovery and development is an important area of research for pharmaceutical industries and medicinal chemists. This classical approach demanded significant investments of time and resources to bring a single drug to market. Furthermore, the complexity and vast scale of data from genomics, proteomics, microarrays, and clinical trials present significant challenges in the drug discovery pipeline. Nevertheless, bioinformatics, pharmacoinformatics, and cheminformatics technologies have been developed thanks to breakthroughs in computational methodologies and a surge in multi-omics data, drastically shortening the time it takes to create new drugs. Large amounts of biological data stored in global databases are the building blocks for machine learning and deep learning methods. They make it easier to find patterns and models that can help find therapeutically active molecules with less time, work, and money. Machine learning and deep learning technology are vital in drug design and development. We have applied these algorithms to various drug discovery processes such as protein structure prediction, toxicity prediction, oral bioavailability prediction, de novo design of new chemical scaffolds, structure-based and ligand-based virtual screening, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, and clinical trial design. Historical evidence underscores the successful implementation of AI and deep learning in this domain. Finally, we highlight some successful machine learning or deep learning-based models employed in the drug design and development pipeline. Furthermore, there has been a notable increase in interest regarding the application of AI technology in hospital pharmacy settings, which has been discussed in this review. This review will be invaluable to medicinal and computational chemists seeking DL tools for drug discovery projects and hospital pharmacies.

药物发现和开发是制药工业和药物化学家研究的一个重要领域。这种传统的方法需要投入大量的时间和资源来将一种药物推向市场。此外，基因组学、蛋白质组学、微阵列和临床试验数据的复杂性和规模对药物发现管道提出了重大挑战。然而，由于计算方法的突破和多组学数据的激增，生物信息学、药物信息学和化学信息学技术得到了发展，大大缩短了创造新药所需的时间。存储在全球数据库中的大量生物数据是机器学习和深度学习方法的基石。它们可以更容易地找到模式和模型，从而帮助用更少的时间、工作和金钱找到具有治疗活性的分子。机器学习和深度学习技术在药物设计和开发中至关重要。我们已经将这些算法应用于各种药物发现过程，如蛋白质结构预测、毒性预测、口服生物利用度预测、新化学支架的从头设计、基于结构和基于配体的虚拟筛选、药效团建模、定量结构-活性关系、药物重新定位和临床试验设计。历史证据强调了人工智能和深度学习在这一领域的成功实施。最后，我们重点介绍了在药物设计和开发管道中使用的一些成功的机器学习或基于深度学习的模型。此外，对于人工智能技术在医院药房环境中的应用，人们的兴趣也显著增加，这在本文中进行了讨论。这篇综述将是非常宝贵的药物和计算化学家寻求DL工具的药物发现项目和医院药房。

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引用次数: 0

Insulin delivery devices in diabetes management: Applications and advancements 胰岛素输送装置在糖尿病管理：应用和进展

Intelligent Pharmacy

Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2025.02.002

Runhuang Yang, Zongwen Yang, Jingnan Chi, Ya Zhu

With continuous advancements in diabetes management technology, insulin delivery devices have become increasingly central to the treatment of diabetes. This review discusses the applications and development of various insulin delivery technologies, including insulin pens and pumps, in the management of type 1 diabetes (T1DM) and type 2 diabetes (T2DM). Insulin pens are widely used among individuals with T2DM due to their ease of use and dosing accuracy. The recent development of smart insulin pens has further enhanced patient adherence and glycemic control. Insulin pumps, particularly patch pumps, provide more precise glucose management for individuals with T1DM and select T2DM patients, significantly reducing glycemic variability and the risk of hypoglycemia. Patch pumps, as an innovative insulin infusion device, are particularly suitable for patients requiring discreet and convenient use, owing to their compact, lightweight, and tubeless design. This is especially pertinent for the large population of individuals with T2DM. However, mechanical patch pumps still require further optimization, particularly in displaying infusion volume and key operational parameters, to facilitate real-time monitoring and timely therapeutic adjustments by both patients and clinicians. This review summarizes the advantages and limitations of different types of insulin delivery devices and explores their potential role in clinical practice. Further advancements in these systems are expected to offer safer, more convenient, precise, and cost-effective treatment options for diabetes management.

随着糖尿病管理技术的不断进步，胰岛素输送装置在糖尿病治疗中越来越重要。本文综述了胰岛素笔和胰岛素泵等胰岛素输送技术在1型糖尿病（T1DM）和2型糖尿病（T2DM）治疗中的应用和进展。胰岛素笔因其易于使用和给药准确而广泛应用于T2DM患者。最近开发的智能胰岛素笔进一步提高了患者的依从性和血糖控制。胰岛素泵，特别是贴片泵，为T1DM患者和部分T2DM患者提供了更精确的血糖管理，显著降低了血糖变异性和低血糖的风险。贴片泵作为一种创新的胰岛素输注设备，由于其紧凑、轻便和无管的设计，特别适合需要谨慎和方便使用的患者。这对于大量的2型糖尿病患者尤其重要。然而，机械贴片泵仍需要进一步优化，特别是在显示输液量和关键操作参数方面，以便于患者和临床医生实时监测和及时调整治疗。本文综述了不同类型胰岛素输送装置的优点和局限性，并探讨了它们在临床应用中的潜在作用。这些系统的进一步发展有望为糖尿病管理提供更安全、更方便、更精确和更具成本效益的治疗选择。

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引用次数: 0

Smart healthcare: Artificial intelligences impact on drug development and patient care 智能医疗保健：人工智能对药物开发和患者护理的影响

Intelligent Pharmacy

Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2025.01.003

Sachin Mendhi , Krutika Sawarkar , Amruta Shete , Kuldeep Vinchurkar , Sachin S. Mali , Sudarshan Singh , Pooja V. Nagime

The integration of artificial intelligence (AI) into healthcare has catalyzed significant advancements in drug development and patient care, revolutionizing traditional methodologies. This review explores the multifaceted impact of AI on critical areas, highlighting its transformative potential and addressing associated challenges. In drug development, AI facilitates accelerated discovery processes, enhances precision in predicting drug efficacy and safety, and optimizes clinical trial designs. AI-driven technologies such as machine learning (ML) algorithms and deep learning models enable the analysis of vast datasets, leading to the identification of novel therapeutic targets and personalized treatment strategies. In patient care, AI enhances diagnostic accuracy, enables predictive analytics for disease management, and supports telemedicine as well as remote monitoring, thereby improving patient outcomes and accessibility to healthcare services. Despite the promising advancements, the review critically examines the ethical, regulatory, and implementation challenges that accompany AI integration in healthcare. By providing a comprehensive overview of AI's current and potential contributions, this paper aims to provide an elaborative guide that future research and policymaking in smart healthcare.

人工智能（AI）与医疗保健的整合促进了药物开发和患者护理方面的重大进步，彻底改变了传统的方法。本综述探讨了人工智能对关键领域的多方面影响，突出了其变革潜力并解决了相关挑战。在药物开发中，人工智能有助于加速发现过程，提高预测药物疗效和安全性的准确性，并优化临床试验设计。人工智能驱动的技术，如机器学习（ML）算法和深度学习模型，可以对大量数据集进行分析，从而确定新的治疗靶点和个性化治疗策略。在患者护理方面，人工智能提高了诊断准确性，实现了疾病管理的预测分析，并支持远程医疗和远程监控，从而改善了患者的治疗结果和获得医疗保健服务的机会。尽管取得了有希望的进展，但该综述严格审查了人工智能在医疗保健领域整合所带来的伦理、监管和实施挑战。通过对人工智能当前和潜在贡献的全面概述，本文旨在为未来智能医疗的研究和政策制定提供详细的指导。

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引用次数: 0

Artificial intelligence, computational tools and robotics for drug discovery, development, and delivery 用于药物发现、开发和交付的人工智能、计算工具和机器人技术

Intelligent Pharmacy

Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2025.01.001

Ayodele James Oyejide , Yemi Adekola Adekunle , Oluwatosin David Abodunrin , Ebenezer Oluwatosin Atoyebi

The integration of Artificial Intelligence (AI) and robotics into the pharmaceutical sector is rapidly transforming drug discovery, development, and delivery (D-DDD) processes. Traditional drug development is often characterized by lengthy timelines, high costs, and complex challenges associated with target identification, drug efficacy, and safety profiling. AI and robotics offer transformative solutions, bringing speed, precision, and scalability to various stages of D-DDD. In this review, we analyze cutting-edge advancements in AI-driven predictive modeling, machine learning algorithms for molecular screening, and data mining techniques that enable efficient drug target identification and toxicity prediction. We also explore robotics applications that enhance automation in high-throughput screening, compound synthesis, and patient-specific drug delivery systems. Through examining the applications, limitations, and future trends of these technologies, this review provides a comprehensive outlook on the potential of AI and robotics to streamline the drug pipeline and enable personalized therapeutic strategies. Our review reveals that the convergence of AI, robotics, and big data has potential to reshape pharmaceutical research, reduce costs, and pave the way for more accessible, effective therapies. This review thus serves as a critical resource for understanding the future trajectory of intelligent, technology-driven pharmacy and its implications for advancing healthcare.

人工智能（AI）和机器人技术与制药行业的整合正在迅速改变药物发现、开发和交付（D-DDD）流程。传统的药物开发通常具有时间长，成本高，以及与目标识别，药物功效和安全性分析相关的复杂挑战的特点。人工智能和机器人技术提供了变革性的解决方案，为D-DDD的各个阶段带来了速度、精度和可扩展性。在这篇综述中，我们分析了人工智能驱动的预测建模、用于分子筛选的机器学习算法和数据挖掘技术的前沿进展，这些技术能够有效地识别药物靶点和毒性预测。我们还探索了机器人技术在高通量筛选、化合物合成和患者特异性药物输送系统中的应用。通过研究这些技术的应用、局限性和未来趋势，本文全面展望了人工智能和机器人技术在简化药物管道和实现个性化治疗策略方面的潜力。我们的研究表明，人工智能、机器人技术和大数据的融合有可能重塑制药研究，降低成本，为更容易获得、更有效的治疗铺平道路。因此，这篇综述为理解智能、技术驱动的药房的未来发展轨迹及其对推进医疗保健的影响提供了重要资源。

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引用次数: 0

ECLUNG consensus/guidelines development principles and methods (2024 edition) ECLUNG共识/指南制定原则和方法（2024年版）

Intelligent Pharmacy

Pub Date : 2025-04-01 DOI: 10.1016/j.ipha.2024.11.004

Chunwei Xu , Yue Hao , Dong Wang , Shirong Zhang , Wenxian Wang , Qian Wang , Tangfeng Lv , Zhengbo Song , Ziming Li

引用次数: 0

Evaluation of the hypoglycemic and hypotensive efficacy of sodium-glucose cotransporter-2 inhibitors in patients with type 2 diabetes: A model-based dose–response network meta-analysis 评价钠-葡萄糖共转运蛋白-2抑制剂对2型糖尿病患者的降糖降压效果：基于模型的剂量-反应网络meta分析

Intelligent Pharmacy

Pub Date : 2025-04-01 DOI: 10.1016/j.ipha.2025.02.001

Sanbao Chai , Fengqi Liu , Pei Li , Siyan Zhan , Feng Sun

Aims

To study the dose effect relationship of sodium-glucose cotransporter-2 inhibitor (SGLT-2i) in reducing blood glucose and blood pressure in type 2 diabetes mellitus (T2DM).

Materials and methods

We searched PubMed, Embase, Web of Science, Cochrane Library, and clinicaltrials.gov for related literature, with the search period spanning from the establishment of each platform to May 1, 2024. The main analysis method used is model-based network meta-analysis.

Results

A total of 192 RCTs involving 67,677 patients with T2DM were included in this study. The results showed that SGLT-2i reduced glycated hemoglobin A1c (HbA1c) in T2DM by 0.50 % (95 % CI: 0.49 % ∼ 0.50 %) compared with placebo. The hypoglycemic effects of Luseogliflozin and Henagliflozin on HbA1c ranked first and second, with values of 0.92 % (95 % CI: 0.61 % ∼ 1.28 %) and 0.91 % (95 % CI: 0.61 % ∼ 1.36 %), respectively. Compared with placebo, the results showed that SGLT-2i lowered systolic blood pressure (SBP) by 3.23 mmHg (95 % CI: 3.19 mmHg ∼ 3.26 mmHg) and diastolic blood pressure (DBP) by 4.16 mmHg (95 % CI: 4.13 mmHg ∼ 4.18 mmHg) in patients with T2DM, respectively. Canagliflozin showed the greatest reduction in SBP and Luseogliflozin showed the greatest reduction in DBP, respectively.

Conclusions

The effect of SGLT-2i in reducing HbA1c in patients with T2DM increased with increasing daily dose, with Luseogliflozin and Henagliflozin being the most effective. SGLT-2i significantly reduced both SBP and DBP in T2DM, but there was no significant dose–response relationship. Among the SGLT-2i, Canagliflozin and Luseogliflozin exhibited better antihypertensive effects.

目的研究钠-葡萄糖共转运蛋白-2抑制剂（SGLT-2i）对2型糖尿病（T2DM）降糖降压的剂量效应关系。材料与方法检索PubMed、Embase、Web of Science、Cochrane Library、clinicaltrials.gov等相关文献，检索时间从各平台建立至2024年5月1日。采用的主要分析方法是基于模型的网络元分析。结果共纳入192项随机对照试验，共纳入67,677例T2DM患者。结果显示，与安慰剂相比，SGLT-2i可将T2DM患者的糖化血红蛋白（HbA1c）降低0.50% （95% CI: 0.49% ~ 0.50%）。鲁西格列净和亨纳格列净对HbA1c的降糖作用排名第一和第二，分别为0.92% （95% CI: 0.61% ~ 1.28%）和0.91% （95% CI: 0.61% ~ 1.36%）。与安慰剂相比，结果显示SGLT-2i使T2DM患者的收缩压（SBP）分别降低3.23 mmHg （95% CI: 3.19 mmHg ~ 3.26 mmHg）和舒张压（DBP）分别降低4.16 mmHg （95% CI: 4.13 mmHg ~ 4.18 mmHg）。卡格列净和卢西格列净分别表现出最大的收缩压和舒张压的降低。结论SGLT-2i降低T2DM患者HbA1c的作用随着日剂量的增加而增强，以鲁西格列净和亨纳格列净效果最好。SGLT-2i可显著降低T2DM患者的收缩压和舒张压，但无显著的剂量-反应关系。SGLT-2i中，卡格列净和卢西格列净降压效果较好。

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引用次数: 0

Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning 用人工智能增强药物发现：使用图神经网络和集成学习的药代动力学预测建模

Intelligent Pharmacy

Pub Date : 2025-04-01 DOI: 10.1016/j.ipha.2024.11.002

R. Satheeskumar

Accurately predicting pharmacokinetic (PK) parameters such as absorption, distribution, metabolism, and excretion (ADME) is essential for optimizing drug efficacy, safety, and development timelines. Traditional experimental methods are often slow and expensive, driving the need for advanced AI-based approaches in PK modeling. This study compares cutting-edge machine learning models, including Graph Neural Networks (GNNs), Transformers, and Stacking Ensembles, against traditional models like Random Forest and XGBoost, using a dataset of over 10,000 bioactive compounds from the ChEMBL database. The Stacking Ensemble model achieved the highest accuracy (R² of 0.92, MAE of 0.062), outperforming GNNs (R² of 0.90) and Transformers (R² of 0.89). These AI models excelled in capturing complex molecular interactions and long-range dependencies, significantly improving PK predictions. The high accuracy achieved (R² = 0.92) by the Stacking Ensemble method indicates that AI models can streamline the drug discovery process by reducing costly in vivo experiments, enabling faster go/no-go decisions during preclinical evaluations, and ultimately accelerating the development of new therapeutics. This reduction in time and cost could facilitate broader industry adoption of AI-driven PK modeling. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters, further enhancing the performance and robustness of these predictive models.

准确预测药代动力学（PK）参数，如吸收、分布、代谢和排泄（ADME），对于优化药物疗效、安全性和开发时间表至关重要。传统的实验方法往往是缓慢和昂贵的，推动需要先进的基于人工智能的方法在PK建模。本研究使用ChEMBL数据库中超过10,000种生物活性化合物的数据集，将包括图神经网络（GNNs）、变形金刚（Transformers）和堆叠集成（Stacking Ensembles）在内的尖端机器学习模型与Random Forest和XGBoost等传统模型进行了比较。堆叠集成模型获得了最高的精度（R2为0.92，MAE为0.062），优于GNNs （R2为0.90）和Transformers （R2为0.89）。这些人工智能模型在捕获复杂的分子相互作用和长期依赖方面表现出色，显著提高了PK预测。堆叠集成方法获得的高准确度（R2 = 0.92）表明，人工智能模型可以通过减少昂贵的体内实验来简化药物发现过程，在临床前评估过程中更快地做出决定，最终加速新疗法的开发。这种时间和成本的减少可以促进更广泛的行业采用人工智能驱动的PK模型。此外，采用贝叶斯优化对超参数进行微调，进一步提高了预测模型的性能和鲁棒性。

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引用次数: 0

Phytochemical profiling, FT-IR spectroscopy, and antioxidant evaluation of select Lamiaceae species Lamiaceae植物化学分析、FT-IR光谱及抗氧化评价

Intelligent Pharmacy

Pub Date : 2025-04-01 DOI: 10.1016/j.ipha.2024.09.009

J. Joselin , B.S. Benila , T.S. Shynin Brintha , S. Jeeva

The present study aimed to conduct a preliminary phytochemical analysis of aqueous, ethanol, and hexane leaf extracts from Anisomeles malabarica (L.) R.Br. ex Sims, Leucas aspera (Willd.) Link., Ocimum tenuiflorum L., and Plectranthus amboinicus (Lour.) Spreng. The analysis revealed the presence of terpenoids, saponins, glycosides, phenolics, fats, oils, tannins, quinines, and phlobatannins. Quantitative measurements showed soluble sugars at 1.92, 3.76, 2.45, and 2.07 mg/g, amino acids at 0.53, 4.74, 2.3, and 3.25 mg/g, and proteins at 5.43, 2.8, 7.32, and 4.75 mg/g, respectively. Flavonoid contents were 2.43, 6.85, 4.8, and 3.20 μg/g. Phenolic content was highest in Anisomeles malabarica (1.2 mg/g). Chlorophyll levels ranged from 0.4 to 2.15 mg/g, while carotenoids were highest in Plectranthus amboinicus (5.45 μg/g). All leaf extracts exhibited hydroxyl radical and superoxide anion scavenging activities, which increased with extract concentration. FT-IR analysis confirmed the presence of various functional groups. These findings suggest the potential of these Lamiaceae leaves in developing antibiotics and insecticides.

本研究的目的是对含水、乙醇和己烷提取物进行初步的植物化学分析。R.Br。前Sims， Leucas aspera （wild）链接。(3)、芦花苜蓿（Ocimum tenuflorum L.）和凤梨（Plectranthus amboinicus）。Spreng。分析结果显示，其中含有萜类、皂苷、糖苷、酚类、脂肪、油、单宁、奎宁和酞菁。定量测定表明，可溶性糖含量分别为1.92、3.76、2.45和2.07 mg/g，氨基酸含量分别为0.53、4.74、2.3和3.25 mg/g，蛋白质含量分别为5.43、2.8、7.32和4.75 mg/g。黄酮含量分别为2.43、6.85、4.8和3.20 μg/g。其中，马来茴香的酚类含量最高，为1.2 mg/g。叶绿素含量为0.4 ~ 2.15 mg/g，类胡萝卜素含量最高（5.45 μg/g）。各叶提取物均具有清除羟基自由基和超氧阴离子的活性，且随提取物浓度的增加而增强。FT-IR分析证实了各种官能团的存在。这些发现表明，这些扇叶科植物具有开发抗生素和杀虫剂的潜力。

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引用次数: 0

Advancements in contemporary pharmacological innovation: Mechanistic insights and emerging trends in drug discovery and development 当代药理学创新的进展：药物发现和开发的机制见解和新趋势

Intelligent Pharmacy

Pub Date : 2025-04-01 DOI: 10.1016/j.ipha.2024.10.001

Sanjoy Majumder , Gagan Kumar Panigrahi

Developing a new drug and bringing it to the market is a complex and time-consuming process that involves multiple phases of drug discovery and development. However, recent advancements in various technologies, such as multi-omics, genome editing, Artificial Intelligence (AI), and Machine Learning (ML), have significantly improved this process. These technologies have made the process more accurate, less time-consuming, and cost-effective compared to the conventional methods of drug discovery and development. In the current age, discovering and developing drugs is a collaborative effort that involves scientific breakthroughs, technological advancements, and regulatory oversight. The pharmaceutical industry is constantly innovating new techniques, fostering interdisciplinary collaboration, and prioritizing patient-centered approaches. In this review, we explore the latest and most updated information about using advanced technologies in drug discovery. The review begins by briefly explaining the conventional drug discovery and development process, and then delves into the applications of multi-omics, genome editing technology, systems biology, artificial intelligence, and machine learning.

开发一种新药并将其推向市场是一个复杂而耗时的过程，涉及药物发现和开发的多个阶段。然而，最近各种技术的进步，如多组学、基因组编辑、人工智能（AI）和机器学习（ML），极大地改善了这一过程。与传统的药物发现和开发方法相比，这些技术使该过程更准确，更省时，更具成本效益。在当今时代，发现和开发药物是一项涉及科学突破、技术进步和监管监督的合作努力。制药行业正在不断创新新技术，促进跨学科合作，并优先考虑以患者为中心的方法。在这篇综述中，我们探讨了在药物发现中使用先进技术的最新和最新的信息。本综述首先简要介绍了传统药物的发现和开发过程，然后深入探讨了多组学、基因组编辑技术、系统生物学、人工智能和机器学习的应用。

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引用次数: 0