Pharmaceutical Medicine最新文献

Purpose: The Summary of Product Characteristics (SmPC) is required to provide unambiguous information on the authorized use of a medicinal product. Therefore, we performed a structured analysis of the information provided for pediatric patients in current SmPCs.

Methods: In the German SmPC of the medicinal products of 452 active substances, we analyzed for each of the listed indications whether information on pediatric use was available in Sects. 4.1-4.4 of the SmPC and, if so, whether it was unambiguous. Information was considered unambiguous if it indicated an exact age- or weight-related specification. The analysis also considered the type of marketing authorization and the date of marketing authorization, either before or after the Pediatric Regulation 2007 came into force.

Results: Among the 30,354 identified indications in 8464 SmPCs, unambiguous information was found for 72.4% (21,974/30,354) of the indications. Of these, 45.4% (9967/21,974) disclosed a contraindication for the entire population under 18 years of age. The proportion of unambiguous information was higher for medicinal products with centralized marketing authorization (86.5% [1449/1676]) than for those with a national one (71.6% [20,525/28,678]; p < 0.001). A higher proportion of unambiguous information was found for the marketing authorization period 2007-2021 compared with 1996-2006 (1996-2006: 63.8% [7466/11,694]; 2007-2021: 82.1% [12,349/15,040]; p < 0.001).

Conclusion: For about a quarter of all indications, no or only ambiguous information was available for pediatric patients. The measures initiated in recent years to increase pediatric-specific information in SmPCs should be intensified in order to improve drug safety in children and adolescents.

Background: The Sleep Diary Questionnaire (SDQ), a modified version of the Consensus Sleep Diary, is a 17-item sleep diary for assessing subjective total sleep time (sTST: total time spent asleep at night) and other sleep parameters in insomnia trials. sTST is a key parameter of efficacy in insomnia trials; however, the magnitude of improvement in this parameter that people with insomnia disorder consider clinically meaningful is unclear.

Objective: The aim of this study was to estimate meaningful within-patient change for sTST using clinical trial data.

Methods: Data were from an open-label trial of zolpidem and pooled data from a phase III placebo-controlled trial of daridorexant. In both trials, adults with moderate to severe insomnia completed the SDQ daily. Meaningful change in sTST was estimated in an anchor-based analysis using outcome measures that were correlated with change in weekly average sTST (Spearman correlation coefficient ≥ 0.30): the Insomnia Severity Index, patient global assessments and impressions of severity and change in daytime and night-time symptoms (PGA-S, PGI-S, PGI-C), and clinician global impressions of severity and change in patients' daytime symptoms (CGI-S, CGI-C). Meaningful within-patient change estimates were 'triangulated' to identify a value where they converged.

Results: In the open-label trial (N = 114), subjects with a 1-point or 1-step improvement on the anchors had mean increases in sTST of 60.1-83.2 min at day 8 and 55.5-68.2 min at day 15. For subjects with a 2-point or 2-step improvement on the anchors, mean increases in sTST were 79.6-81.4 min at day 8 and 80.1-93.5 min at day 15. In the phase III trial (N = 930), weekly average increases in sTST for subjects with a 1-point or 1-step improvement on the anchors were 39.3-46.7 min at month 1 and 47.3-58.3 min at month 3. For subjects with a 2-point or 2-step improvement on the anchors, mean increases in sTST were 60.7-76.2 min at month 1 and 70.1-87.7 min at month 3. Triangulation of these values supported a meaningful within-patient change threshold starting at 55 min.

Conclusion: Increasing sTST is an important treatment outcome for people with insomnia. An increase in sleep time of approximately 55 min is meaningful to patients.

Clinical trials registration: NCT03056053 (17 February 2017) and NCT03545191 (4 June 2018).

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.

Decentralised clinical trials (DCTs) encompass various terms such as virtual, home-based, remote and siteless trials. The objectives of DCTs are to enhance the ease of participation for patients in clinical trials by minimising or removing the necessity for trial subjects to travel to the trial sites. This approach has been shown to reduce drop-out rates, increase study effectiveness and ultimately get life-altering drugs to market faster-saving sponsors billions. At the outset, DCTs deploy a wide range of digital technologies to collect safety and efficacy data from study participants, providing study treatments and performing investigations from the comfort of the patient's own home. The aim of decentralised trials includes patient centricity, enhanced efficacy in clinical trial conduct and generating real-world data. This is done by not only making it convenient for the patient to participate in the trial execution, but also involving them from the planning stage and taking their inputs during designing of trials and consenting documentation, understanding their treatment requirements and designing the studies accordingly. Various regulatory authorities have published guidelines governing DCT principles, especially after the coronavirus disease 2019 (COVID-19) experience of undertaking multicentric clinical trials. Both United States Food and Drug Administration (USFDA) and European Medicines Agency (EMA) have newer, recently updated guidelines to capture this growing reality to undertake clinical trials using patient technology or patient-centric technologies. Other regulatory agencies are accepting data generated using decentralised and patient-centric technologies and making an effort to include elements of decentralised trials in their regulatory guidelines. Decentralised trials follow a hybrid approach to have a balanced mix of remote and in-person data collection and trial procedures. Decentralised and patient-centric approaches are the future of any organisation for the conduct of clinical trials. Globally, all sponsor pharmaceutical companies must start undertaking drug development and clinical trials using a decentralised approach while keeping patient centricity in mind.

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.

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.

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.

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.