Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Background and objective Drugs that act on the central nervous system have a high potential to cause drug-related problems (DRPs). A clinical pharmacist aided by collaborative efforts within an interdisciplinary healthcare team can prevent, detect, and resolve DRPs, thereby contributing to the promotion of medication safety and improving the quality of life of individuals under care. This study aimed to assess DRPs identified in the neurology ward of a tertiary hospital from February 2016 to November 2019. Methods This was a descriptive study with a cross-sectional and retrospective design involving secondary data collected from pharmaceutical care (PC) records. Student\'s t-tests, Pearson correlation coefficients, Poisson models, and logistic regression models were used to analyze the associations between age, number and type of medications, duration of hospitalization, and the occurrence of DRPs. Results A total of 130 patients were included in the study, and a total of 266 DRPs were detected, with 93 patients experiencing more than one DRP and 37 not presenting any DRPs. Necessity-related DRPs were the most prevalent (46.6%) type, followed by safety-related DRPs (28.6%). The prevalence of safety-related DRPs was higher in individuals older than 60 years (p<0.001). Conclusions Of note, 84.6% of the interventions suggested by pharmacists to resolve DRPs were accepted by the healthcare team. The high number of DRPs found underscores the importance of the clinical role of the pharmacist and interprofessional collaboration in the care of neurological patients, especially in the pharmaceutical follow-up of elderly individuals.

The protein-ligand binding frequently occurs in living organisms and plays a crucial role in the execution of the functions of proteins and drugs. It is also an indispensable part of drug discovery and screening. While the methods for investigating protein-ligand binding are diverse, each has its own objectives, strengths, and limitations, which all influence the choice of method. Many studies concentrate on one or a few specific methods, suggesting that comprehensive summaries are lacking. Therefore in this review, these methods are comprehensively summarized and are discussed in detail: prediction and simulation methods, thermal and thermodynamic methods, spectroscopic methods, methods of determining three-dimensional structures of the complex, mass spectrometry-based methods and others. It is also important to integrate these methods based on the specific objectives of the research. With the aim of advancing pharmaceutical research, this review seeks to deepen the understanding of the protein-ligand binding process.

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UNASSIGNED: Understanding the cost of drug development can help inform the development of policies to reduce costs, encourage innovation, and improve patient access to drugs.
UNASSIGNED: To estimate the cost of drug development by therapeutic class and trends in pharmaceutical research and development (R&D) intensity over time.
UNASSIGNED: In this economic evaluation study, an analytical model of drug development constructed using public and proprietary sources that collectively cover data from 2000 to 2018 was used to estimate the cost of bringing a drug to market, overall and for specific therapeutic classes. The analysis for the study was completed in October 2020.
UNASSIGNED: Three measures of development cost from nonclinical through postmarketing stages were estimated: mean out-of-pocket cost or cash outlay, mean expected cost, and mean expected capitalized cost. Pharmaceutical R&D intensity, defined as the ratio of R&D spending to total sales, from 2008 to 2019, based on the time frame for available data, was also analyzed.
UNASSIGNED: The estimated mean cost of developing a new drug was approximately $172.7 million (2018 dollars) (range, $72.5 million for genitourinary to $297.2 million for pain and anesthesia), inclusive of postmarketing studies. The cost increased to $515.8 million when cost of failures was included. When the costs of failures and capital were included, the mean expected capitalized cost of drug development increased to $879.3 million (range, $378.7 million for anti-infectives to $1756.2 million for pain and anesthesia); results varied widely by therapeutic class. The pharmaceutical industry as a whole experienced a decline of 15.6% in sales but increased R&D intensity from 11.9% to 17.7% from 2008 to 2019. By contrast, R&D intensity of large pharmaceutical companies increased from 16.6% to 19.3%, whereas sales increased by 10.0% (from $380.0 to $418.0 billion) over the same 2008 to 2019 period, even though the cost of drug development remained relatively stable or may have even decreased.
UNASSIGNED: In this economic evaluation of new drug development costs, even though the cost of drug development appears to have remained stable, R&D intensity of large pharmaceutical companies remained relatively unchanged, despite substantial growth in revenues during this period. These findings can inform the design of drug-related policies and their potential impacts on innovation and competition.

This article is an autobiographical account of a research career in inflammatory diseases, mechanisms and pharmacotherapy, drug research and development, in academia and industry in various European countries spanning the last 55 years. The author describes how tenacity and independent thought, learned in formative years, and tempered later by the development of good relationships with colleagues have guided his career. This has spanned research, among other fields, on prostaglandins as pro-and anti-inflammatory mediators, oxidative stress and antioxidants, phospholipid mediators, cytokines, innate and adaptive immune responses and the establishment of various inflammatory and immunological models. The author has helped discover and develop novel therapeutic approaches to pain, arthritic, dermatological, respiratory, and autoimmune disorders and contributed to bringing eight drug candidates to clinical trials. He has helped establish new research labs in four different centres and been involved in teaching undergraduate and mature students in three different universities. With extensive experience in scientific publishing and several international awards, he emphasises that without good teamwork, little can be achieved in scientific research.

In vitro models of the human liver are promising alternatives to animal tests for drug development. Currently, primary human hepatocytes (PHHs) are preferred for pharmacokinetic and cytotoxicity tests. However, they are unable to recapitulate the flow of bile in hepatobiliary clearance owing to the lack of bile ducts, leading to the limitation of bile analysis. To address the issue, a liver organoid culture system that has a functional bile duct network is desired. In this study, we aimed to generate human iPSC-derived hepatobiliary organoids (hHBOs) consisting of hepatocytes and bile ducts. The two-step differentiation process under 2D and semi-3D culture conditions promoted the maturation of hHBOs on culture plates, in which hepatocyte clusters were covered with monolayered biliary tubes. We demonstrated that the hHBOs reproduced the flow of bile containing a fluorescent bile acid analog or medicinal drugs from hepatocytes into bile ducts via bile canaliculi. Furthermore, the hHBOs exhibited pathophysiological responses to troglitazone, such as cholestasis and cytotoxicity. Because the hHBOs can recapitulate the function of bile ducts in hepatobiliary clearance, they are suitable as a liver disease model and would be a novel in vitro platform system for pharmaceutical research use.

Model-based meta-analysis (MBMA) can be used in assisting drug development and optimizing treatment in clinical practice, potentially reducing costs and accelerating drug approval. We aimed to assess the application and quality of MBMA studies. We searched multiple databases to identify MBMA in pharmaceutical research. Eligible MBMA should incorporate pharmacological concepts to construct mathematical models and quantitatively examine and/or predict drug effects. Relevant information was summarized to provide an overview of the application of MBMA. We used AMSTAR-2 and PRISMA 2020 checklists to evaluate the methodological and reporting quality of included MBMA, respectively. A total of 143 MBMA studies were identified. MBMA was increasingly used over time for one or more areas: drug discovery and translational research (n = 8, 5.6%), drug development decision making (n = 42, 29.4%), optimization of clinical trial design (n = 46, 32.2%), medication in special populations (n = 15, 10.5%), and rationality and safety of drug use (n = 71, 49.7%). The included MBMA covered 17 disease areas, with the top three being nervous system diseases (n = 19, 13.2%), endocrine/nutritional/metabolic diseases (n = 17, 11.8%), and neoplasms (n = 16, 11.1%). Of these MBMA studies, 138 (96.5%) were rated as very low quality. The average rate of compliance with PRISMA was only 51.4%. Our findings suggested that MBMA was mainly used to evaluate the efficacy and safety of drugs, with a focus on chronic diseases. The methodological and reporting quality of MBMA should be further improved. Given AMSTAR-2 and PRISMA checklists were not specifically designed for MBMA, adapted assessment checklists for MBMA should be warranted.

Malaria vaccine introduction in endemic countries is a game-changing milestone in the fight against the disease. This article examines the inequity in the global pharmaceutical research, development, manufacturing, and trade landscape. The role of inequity in hindering progress towards malaria elimination is explored. The analysis finds that transformational changes are required to create an equity-enabling environment. Addressing the inequity is critical to maximizing the public health impact of vaccines and attaining sustainability. Avenues to catalyze progress by leveraging malaria vaccines and messenger ribonucleic acid (mRNA) technology are discussed.

Real-world data (RWD) can provide intel (real-world evidence, RWE) for research and development, as well as policy and regulatory decision-making along the full spectrum of health care. Despite calls from global regulators for international collaborations to integrate RWE into regulatory decision-making and to bridge knowledge gaps, some challenges remain. In this work, we performed an evaluation of Austrian RWD sources using a multilateral query approach, crosschecked against previously published RWD criteria and conducted direct interviews with representative RWD source samples. This article provides an overview of 73 out of 104 RWD sources in a national legislative setting where major attempts are made to enable secondary use of RWD (e.g. law on the organisation of research, \"Forschungsorganisationsgesetz\"). We were able to detect omnipresent challenges associated with data silos, variable standardisation efforts and governance issues. Our findings suggest a strong need for a national health data strategy and data governance framework, which should inform researchers, as well as policy- and decision-makers, to improve RWD-based research in the healthcare sector to ultimately support actual regulatory decision-making and provide strategic information for governmental health data policies.