Pharmaceutical Medicine最新文献

Oncology Global Safety Teams (GSTs) are not universally tasked with the development of the risk section of the products target product profile (TPP). This fact makes little sense since the GST is tasked by the company to identify, analyze, and mitigate a product's risks. The TPP, in essence, establishes boundaries for go/no-go decisions around a product or products in combination treatment. Involvement of the Oncology GST in producing a well-researched and evidenced based TPP safety section allows the team to develop knowledge around the drug(s) studied or added to a study arm. The increased use of umbrella and platform studies for early-phase oncology trials allows an excellent resource for the use of clinical data to estimate the risk of developmental drugs combined to treat a given oncology indication. To shorten time to marketing, companies are including developmental products with novel mechanisms early within their development cycles. Antibody drug conjugates (ADCs) and bi-directional antibodies are a few examples of products combined in arms of a platform or umbrella study early and with only immature clinical data available. This article will share a novel analytical approach for safety teams to develop a well thought-out and defendable safety section to the TPP. Strategies to estimate the risks associated with combination therapies will be brought forward. The advantages of having the safety team involved early in the benefit/risk, go/no-go decisions for a study or the addition of a study arm will be detailed. The early development of a well-documented TPP will enhance chances of a successful product submission.

The coronavirus disease 2019 (COVID-19) pandemic highlighted the world's level of preparedness in managing public health emergencies (PHEs). It revealed the critical need for timely medical therapeutics, especially vaccines. To expedite response, many nations, including India, adopted emergency approval mechanisms and offered new ways of review, such as the rolling review along with the accelerated review procedure. This response resulted in reallocating internal resources and adopting new policies and measures, such as integrating digital technology with regulatory submissions and flexibility in statistical approaches. The present review focuses on the utilization of the New Drugs and Clinical Trials Rules 2019 for granting emergency approval to COVID-19 vaccines by the Drug Controller General of India (DCGI) and explores the legislative basis for such authorization during the PHE. The review aims to elucidate key intricacies and challenges inherent in the existing 'emergency use' framework within the Indian regulatory landscape. It assesses three critical facets of the 'emergency use' paradigm: the definition of the term, establishment of a transparent decision-making process, and formulation of rules governing termination or expiration of the emergency status. It makes policy recommendations regarding the 'emergency use' framework to respond to new, emerging, or re-emerging public health threats of the future.

Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.

Background: Cyproterone acetate (CPA) is a synthetic progesterone derivative introduced in the 1970s and prescribed as antiandrogenic therapy for inoperable prostate cancer, sexual deviations in men, and signs of androgenization in women. In 2020, the CPA summary of product characteristics (SmPC) was revised to include an updated special warning and precaution about (1) the risk of meningioma with increasing cumulative dose and (2) contraindication in patients with meningioma or history of meningioma. A Direct Healthcare Professional Communication (DHPC) was distributed. The European Medicine Agency's Pharmacovigilance Risk Assessment Committee requested that marketing authorization holders in Europe conduct a survey to assess physicians' knowledge of the updated key safety information. The primary objective of this study was to measure physicians' awareness (i.e., did they receive and review the revised SmPC and DHPC) and level of knowledge and understanding of the key safety information pertaining to the restricted use of CPA monotherapy because of the risk of meningioma.

Methods: This cross-sectional web-based survey was administered to dermatologists, endocrinologists, gynecologists, urologists, oncologists, psychiatrists, and general practitioners in France, Germany, Poland, Spain, and the Netherlands who had prescribed CPA monotherapy in the previous 12 months to assess awareness of the risk of meningioma associated with CPA monotherapy.

Results: Of the 613 physicians who participated, 85% correctly indicated that CPA monotherapy should be prescribed with the lowest effective dose, 75% correctly indicated that the risk of meningioma increases with increasing cumulative CPA monotherapy doses, and 73% correctly indicated that treatment with CPA-containing products must be stopped permanently if a patient is diagnosed with meningioma. Overall, 40% of physicians reported having received the DHPC, and 42% reported having received the revised SmPC.

Conclusions: Despite low recall of receipt of the updated SmPC and DHPC, most physicians surveyed are aware of the meningioma risk and actions to mitigate the risk.

The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.

Immune checkpoint inhibitors (ICIs) have revolutionised the treatment landscape across many solid organ malignancies and form part of routine clinical practice in many tumours. As indications for monotherapy, doublet therapy and combination approaches with chemotherapy and targeted agents expand, clinicians must be aware of the wide range of possible immune-related adverse events (irAEs). Common toxicities, including rash, colitis, hepatitis and pneumonitis are well described in the literature, and have established diagnostic and management algorithms. Rarer toxicities, often with an incidence of less than 1%, are less defined. These syndromes can be poorly recognised, may take on a fulminant course and do not have established or evidence-based diagnostic and management strategies. As such, patients may experience increased morbidity, mortality and poorer outcomes, related both to these irAEs as well as how the treatment of these may affect the management of their underlying malignancy. In this review, we aim to explore the incidence, potential biomarkers, pathogenesis, diagnostic work-up and clinical sequelae of a selection of uncommon irAEs, with a focus on myocarditis, neurological and haematologic syndromes. Further prospective research is required to accurately define the incidence and pathogenesis of these conditions, with the aim of increasing clinician awareness of rare irAEs and to assist with a more personalised and mechanism-based approach to these syndromes.

Introduction: Qualitative and quantitative methods provide different and complementary insights into patients' preferences for treatment.

Objective: The aim of this study was to use a novel, mixed-methods approach employing qualitative and quantitative approaches to generate preliminary insights into patient preferences for the treatment of a rare disease-generalized myasthenia gravis (gMG).

Methods: We conducted a mixed-methods study to collect exploratory qualitative and quantitative patient preference information and generate informative results within a condensed timeline (about 4 months). Recruitment was facilitated by an international health research firm. Study participants first reviewed a brief document describing six treatment attributes (to facilitate more efficient review of the material during the focus groups) and were then provided a link to complete an online quantitative survey with a single risk threshold task. They then participated in online focus groups, during which they discussed qualitative questions about their experience with gMG treatment and completed up to three quantitative threshold tasks, the first of which repeated the threshold task from the online survey.

Results: The study elicited both quantitative data on 18 participants' risk tolerance and qualitative data on their treatment experience, additional treatment attributes of importance, the reasoning behind their preferences, and the trade-offs they were willing to make. Most participants (n = 15) chose the same hypothetical treatment in the first threshold task in the online survey and the focus groups. Focus group discussions provided insights into participants' choices in the threshold tasks, confirmed that all the attributes were relevant, and helped clarify what was important about the attributes.

Conclusions: Patient preference information can be collected using a variety of approaches, both qualitative and quantitative, tailored to fit the research needs of a study. The novel mixed-methods approach employed in this study efficiently captured patient preference data that were informative for exploratory research, internal decision making, and future research.

The objective of this current opinion paper is to draw global attention to medication adherence, emphasizing its crucial role in drug trials. Frequently, trialists lean on traditional approaches to assess medication adherence, which, while comfortable, may only reveal what trialists desire rather than offering the essential insights needed for informed decision making in drug development. Understanding drug exposure and medication adherence is paramount when evaluating the effectiveness and safety of investigational medications. Without a comprehensive understanding of how patients adhere to their prescribed treatment regimens, the integrity and dependability of clinical trial results can be compromised. This paper emphasizes the need for measures that accurately and reliably assess medication intake behaviors, enabling the differentiation between minor dosing errors and significant deviations that may impact the drug's efficacy and safety. Accurate knowledge of drug exposure empowers researchers to make informed decisions, identify potential confounding factors, and appropriately interpret study outcomes, ultimately ensuring the validity and reliability of the research findings. By prioritizing drug exposure assessment and medication adherence measurement, clinical trials can enhance their scientific rigor, contribute to more accurate evaluations of investigational medications, and ultimately speed up the development process.