Clinical trial design in the era of precision medicine

Adaptive Universal Principles for Real-world Observational Studies (AUPROS): an approach to designing real-world observational studies for clinical, epidemiologic, and precision oncology research

The field of precision oncology has witnessed several advances that stimulated the development of new clinical trial designs and the emergence of real-world data (RWD) as an important resource for evidence generation in healthcare decision-making. Here, we highlight our experience with an innovative approach to a set of Adaptive, Universal Principles for Real-world Observational Studies (AUPROS). To demonstrate the utility of these principles, we used a mixed-methods approach to assess three studies that follow AUPROS at Princess Margaret Cancer Centre: (1) Molecular Epidemiology of ThorAcic Lesions (METAL), (2) Translational Head And NecK Study (THANKS), and (3) CAnadian CAncers With Rare Molecular Alterations (CARMA; NCT04151342). We performed resource assessments, stakeholder-directed surveys and discussions, analysis of funding, research output, collaborations, and a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Based on these analyses, AUPROS is an approach that is applicable to a wide range of observational study designs. The universality of AUPROS allows for multi-purpose analyses of various RWD, and the adaptive nature creates opportunities for multi-source funding and collaborations. Following AUPROS can offer cost and logistical benefits and may lead to increased research productivity. Several challenges were identified pertinent to ethics approvals, sustainability, complex coordination, and data quality that require local adaptation of these principles.

Oronasal mucosal melanoma is defined by two transcriptional subtypes in humans and dogs with implications for diagnosis and therapy

Mucosal melanoma is a rare melanoma subtype associated with a poor prognosis and limited existing therapeutic interventions, in part due to a lack of actionable targets and translational animal models for preclinical trials. Comprehensive data on this tumour type are scarce, and existing data often overlooks the importance of the anatomical site of origin. We evaluated human and canine oronasal mucosal melanoma (OMM) to determine whether the common canine disease could inform the rare human equivalent. Using a human and canine primary OMM cohort of treatment-naive archival tissue, alongside clinicopathological data, we obtained transcriptomic, immunohistochemical, and microbiome data from both species. We defined the transcriptomic landscape in both species and linked our findings to immunohistochemical, microbiome, and clinical data. Human and dog OMM stratified into two distinctive transcriptional groups, which we defined using a species-independent 41-gene signature. These two subgroups are termed CTLA4-high and MET-high and indicate actionable targets for OMM patients. To guide clinical decision-making, we developed immunohistochemical diagnostic tools that distinguish between transcriptomic subgroups. We found that OMM had conserved transcriptomic subtypes and biological similarity between human and canine OMM, with significant implications for patient classification, treatment, and clinical trial design. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Longer survival with precision medicine in late-stage cancer patients

BACKGROUND

In a per-protocol analysis of molecularly profiled patients with treatment-refractory, end-stage cancer discussed at the National Molecular Tumor Board (NMTB), we aimed to assess the overall survival (OS) outcome of targeted treatment compared with no targeted treatment.

MATERIALS AND METHODS

Patients were prospectively included at a single oncological center. Whole exome and RNA sequencing (tumor-normal) were carried out, and cases were presented at the NMTB for discussion of targeted treatment. Treatment was available through a basket trial, by compassionate use or in early clinical trials.

RESULTS

One hundred and ninety-six patients were included from 2020 to 2023. In all but three patients a driver variant was disclosed, while 42% had simultaneous affection of more than three oncogenic pathways. In 42% of patients a druggable target was identified but two-thirds did not receive the suggested treatment. The fraction of patients initiating treatment yearly rose from 8% to 22%. For patients treated (N = 30), the clinical benefit rate was 44% and median time on treatment was 3.5 months. Druggable targets were enriched in lung cancers, while patients receiving or not receiving targeted treatment had similar clinical characteristics. The median OS was longer for patients receiving targeted treatment (15 months), but similar for patients with no druggable target and suggested targeted treatment not initiated (5 and 6 months, respectively) (P = 0.004). In multivariate analysis, targeted treatment (hazard ratio 0.43, confidence interval 0.25-0.72), few metastatic sites, and adenocarcinoma histology were predictive of improved OS while alterations of the RTK/RAS pathway were prognostically unfavorable.

CONCLUSIONS

Tissue-agnostic targeted treatment based on molecular tumor profiling is possible in an increasing fraction of end-stage cancer patients. In those who receive targeted treatment, results strongly suggest a significant survival benefit.

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer

Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression, the full potential of these insights has not yet been fully harnessed in the clinical application of personalized precision medicine for prostate cancer (PCa). In this study, we utilized single-cell sequencing to identify the evolutionary characteristics of tumoral cell states and employed comprehensive bulk RNA sequencing to evaluate their potential as prognostic indicators and therapeutic targets. Leveraging advancements in artificial intelligence, we integrated machine learning with multi-omics to develop and validate the tumor evolutionary characteristic predictive indicator (TECPI). TECPI not only demonstrated superior prognostic performance compared to traditional clinical predictors and 81 previously published models but also improved patient outcomes by accurately identifying individuals who would benefit from immunotherapy and targeted therapies. Furthermore, we experimentally validated the critical role of AMOTL1 in PCa pharmacodynamics through its interaction with AR, pivotal for modulating the sensitivity to AR antagonist. Additionally, we demonstrated the generalizability and applicability of TECPI across pan-cancers. In summary, this study emphasizes the importance of understanding cellular diversity and dynamics within the tumor microenvironment to predict PCa progression and to guide targeted therapy effectively.

A data management system for precision medicine

Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level. LogiqSuite is certified and compliant with international medical data and privacy legislations. This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. The MedDMS is evaluated in seven precision medicine data science projects in prehospital triage, cardiovascular disease, pulmonology, and oncology. The P4O2 consortium uses the MedDMS as an electronic case report form (eCRF) that allows real-time data management and analytics in long covid and pulmonary diseases. In an acute myeloid leukaemia, study data from different sources were integrated to facilitate easy descriptive analytics for various research questions. In the AIDPATH project, LogiqCare is used to process patient data, while LogiqScience is used for pseudonymous CAR-T cell production for cancer treatment. In both these oncological projects the data in LogiqAnalytics is also used to facilitate machine learning to develop new prediction models for clinical-decision support (CDS). The MedDMS is also evaluated for real-time recording of CDS data from U-Prevent for cardiovascular risk management and from the Stroke Triage App for prehospital triage. The MedDMS is discussed in relation to other solutions for privacy-by-design, integrated data stewardship and real-time data analytics in precision medicine. LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. The integrated MedDMS application supports data management for care and research in precision medicine.

Novel Therapeutics in Soft Tissue Sarcoma

There has been noteworthy progress in molecular characterisation and therapeutics in soft tissue sarcomas. Novel agents have gained regulatory approval by the FDA. Examples are the tyrosine kinase inhibitors avapritinib and ripretinib in gastrointestinal stromal tumours (GIST), the immune check point inhibitor atezolizumab in alveolar soft part tissue sarcoma, the γ-secretase inhibitor nirogacestat in desmoid tumours, the NTRK inhibitors larotrectinib and entrectinib in tumours with NTRK fusions, the mTOR inhibitor nab-sirolimus in PEComa, and the EZH-2 inhibitor tazemetostat in epithelioid sarcoma. The FDA has also recently granted accelerated approval for autologous T-cell therapy with afami-cel in patients with HLA-A*02 and MAGE-A4-expressing synovial sarcoma. There are other promising treatments that are still investigational, such as MDM2 and CDK4/6 inhibitors in well-/dedifferentiated liposarcoma, immune checkpoint inhibitors in the head and neck angiosarcoma and a subset of patients with undifferentiated pleomorphic sarcoma, and PARP inhibitors in leiomyosarcoma. The challenges in drug development in soft tissue sarcoma are due to the rarity and the molecular heterogeneity of the disease and the fact that many subtypes are associated with complex karyotypes or non-targetable molecular alterations. We believe that progress maybe possible with a better understanding of the complex biology, the development of novel compounds for difficult targets such as proteolysis targeting chimeras (Protacs), the utilisation of modern clinical trial designs, and enhanced collaboration of academia with industry to develop treatments with a strong biologic rationale.

Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials

Dementia probably due to Alzheimer's disease is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments. In this work, we used a deep learning method to cluster the multivariate disease trajectories of 283 early dementia patients along cognitive and functional scores. Two distinct subgroups were identified that separated patients into 'slow' and 'fast' progressing individuals. These subgroups were externally validated and independently replicated in a dementia cohort comprising 2779 patients. We trained a machine learning model to predict the progression subgroup of a patient from cross-sectional data at their time of dementia diagnosis. The classifier achieved a prediction performance of 0.70 ± 0.01 area under the receiver operating characteristic curve in external validation. By emulating a hypothetical clinical trial conducting patient enrichment using the proposed classifier, we estimate its potential to decrease the required sample size. Furthermore, we balance the achieved enrichment of the trial cohort against the accompanied demand for increased patient screening. Our results show that enrichment trials targeting cognitive outcomes offer improved chances of trial success and are more than 13% cheaper compared with conventional clinical trials. The resources saved could be redirected to accelerate drug development and expand the search for remedies for cognitive impairment.

Precision neurology

Over the past several decades, high-resolution brain imaging, blood and cerebrospinal fluid analyses, and other advanced technologies have changed diagnosis from an exercise depending primarily on the history and physical examination to a computer- and online resource-aided process that relies on larger and larger quantities of data. In addition, randomized controlled trials (RCT) at a population level have led to many new drugs and devices to treat neurological disease, including disease-modifying therapies. We are now at a crossroads. Combinatorially profound increases in data about individuals has led to an alternative to population-based RCTs. Genotyping and comprehensive "deep" phenotyping can sort individuals into smaller groups, enabling precise medical decisions at a personal level. In neurology, precision medicine that includes prediction, prevention and personalization requires that genomic and phenomic information further incorporate imaging and behavioral data. In this article, we review the genomic, phenomic, and computational aspects of precision medicine for neurology. After defining biological markers, we discuss some applications of these "-omic" and neuroimaging measures, and then outline the role of computation and ultimately brain simulation. We conclude the article with a discussion of the relation between precision medicine and value-based care.

Insights on High-Value Procedures From the ISASS 4-Part Webinar Series on Current and Emerging Techniques in Endoscopic Spine Surgery Based on Surgeon Experience

BACKGROUND

The authors conducted a comprehensive review and integration of insights from 4 webinars hosted by the International Society for the Advancement of Spine Surgery (ISASS) to arrive at recommendations for best clinical practices for guideline development for endoscopic spine surgery. This perspective article discusses the limitations of traditional surgical trials and amalgamates surgeons' experience and research on various cutting-edge techniques.

METHODS

Data were extracted from surveys conducted during each webinar session involving 3639 surgeons globally. The polytomous Rasch model was employed to analyze responses, ensuring a robust statistical assessment of surgeon endorsements and educational impacts and focusing on operative nuances and experience-based outcomes. Bias detection was performed using the differential item functioning test.

RESULTS

The ISASS webinars provided a dynamic platform for discussing advances in endoscopic spine surgery, identifying a range of high-value procedures from basic discectomies to complex lumbar interbody fusions. Each high-value endoscopic spine surgery was highlighted in separate peer-reviewed publications, which form the basis for this summary document that synthesizes key takeaways from these webinars. High-value clinical applications of endoscopic spine surgery, primarily defined as higher-intensity endorsement transformation from the pre- to postwebinar survey with a shift to higher mean logit locations of test items both with unbiased and orderly threshold progression, were: (a) Percutaneous interlaminar endoscopic decompression for lateral canal stenosis, (b) transforaminal debridement of low-grade degenerative spondylolisthesis, (c) transforaminal full-endoscopic interbody fusion for hard disc herniation, (d) endoscopic standalone lumbar interbody fusion, (e) endoscopic debridement of spondylolytic spondylolisthesis, and (f) posterior cervical foraminotomy for herniated disc and bony stenosis.

CONCLUSIONS

The ISASS webinar series has significantly impacted surgeons' education and contributed to the identification of high-value endoscopic spine surgery practices that may serve as a cornerstone for surgeon training standards, policy, and guidelines development. Ongoing research on technological advancements and expansions of clinical indications combined with systematic review is expected to refine the recommendations on high-value endoscopic spinal surgeries recommended for enhanced reimbursement.

CLINICAL RELEVANCE

Assessing surgeon confidence and acceptance of endoscopic spinal surgeries using polytomous Rasch analysis.

LEVEL OF EVIDENCE

Level 2 (inferential) and 3 (observational) evidence because Rasch analysis provides statistical validation of instruments rather than direct clinical outcomes.

Highly Multiplexed Tissue Imaging in Precision Oncology and Translational Cancer Research

Precision oncology tailors treatment strategies to a patient's molecular and health data. Despite the essential clinical value of current diagnostic methods, hematoxylin and eosin morphology, immunohistochemistry, and gene panel sequencing offer an incomplete characterization. In contrast, highly multiplexed tissue imaging allows spatial analysis of dozens of markers at single-cell resolution enabling analysis of complex tumor ecosystems; thereby it has the potential to advance our understanding of cancer biology and supports drug development, biomarker discovery, and patient stratification. We describe available highly multiplexed imaging modalities, discuss their advantages and disadvantages for clinical use, and potential paths to implement these into clinical practice. Significance: This review provides guidance on how high-resolution, multiplexed tissue imaging of patient samples can be integrated into clinical workflows. It systematically compares existing and emerging technologies and outlines potential applications in the field of precision oncology, thereby bridging the ever-evolving landscape of cancer research with practical implementation possibilities of highly multiplexed tissue imaging into routine clinical practice.

Risk and Benefit for Basket Trials in Oncology: A Systematic Review and Meta-Analysis

BACKGROUND

Oncology research is increasingly adopting new clinical trial models that implement the concept of precision medicine. One of these is the basket clinical trial design. Basket clinical trials allow new treatments to be evaluated across multiple tumor types. Patients recruited to basket clinical trials share certain molecular characteristics of their cancer that are predictive of clinical benefit from the experimental treatment.

OBJECTIVE

Our aim was to describe the risks and benefits of basket clinical trials in oncology.

METHODS

Our study was prospectively registered in PROSPERO (CRD42023406401). We systematically searched PubMed, Embase, and ClinicalTrials.gov for reports of basket clinical trials in oncology published between 1 January, 2001, and 14 June, 2023. We measured the risk by treatment-related adverse events (grades 3, 4, and 5), and the benefit by objective response rate. We also extracted and analyzed data on progression-free survival and overall survival. When possible, data were meta-analyzed.

RESULTS

We included 126 arms of 75 basket clinical trials accounting for 7659 patients. The pooled objective response rate was 18.0% (95% confidence interval [CI] 14.8-21.1). The rate of treatment-related death was 0.7% (95% CI 0.4-1.0), while 30.4% (95% CI 24.2-36.7) of patients experienced grade 3/4 drug-related toxicity. The median progression-free survival was 3.1 months (95% CI 2.6-3.9), and the median overall survival was 8.9 months (95% CI 6.7-10.2).

CONCLUSIONS

Our results provide an empirical basis for communicating about the risks and benefits of basket clinical trials and for refining new models of clinical trials applied in precision medicine.

Molecular and modular intricacies of precision oncology

Precision medicine is revolutionizing the world in combating different disease modalities, including cancer. The concept of personalized treatments is not new, but modeling it into a reality has faced various limitations. The last decade has seen significant improvements in incorporating several novel tools, scientific innovations and governmental support in precision oncology. However, the socio-economic factors and risk-benefit analyses are important considerations. This mini review includes a summary of some commendable milestones, which are not just a series of successes, but also a cautious outlook to the challenges and practical implications of the advancing techno-medical era.

Organoids-On-a-Chip for Personalized Precision Medicine

The development of personalized precision medicine has become a pivotal focus in modern healthcare. Organoids-on-a-Chip (OoCs), a groundbreaking fusion of organoid culture and microfluidic chip technology, has emerged as a promising approach to advancing patient-specific treatment strategies. In this review, the diverse applications of OoCs are explored, particularly their pivotal role in personalized precision medicine, and their potential as a cutting-edge technology is highlighted. By utilizing patient-derived organoids, OoCs offer a pathway to optimize treatments, create precise disease models, investigate disease mechanisms, conduct drug screenings, and individualize therapeutic strategies. The emphasis is on the significance of this technological fusion in revolutionizing healthcare and improving patient outcomes. Furthermore, the transformative potential of personalized precision medicine, future prospects, and ongoing advancements in the field, with a focus on genomic medicine, multi-omics integration, and ethical frameworks are discussed. The convergence of these innovations can empower patients, redefine treatment approaches, and shape the future of healthcare.

How can quantum computing be applied in clinical trial design and optimization?

Clinical trials are necessary for assessing the safety and efficacy of treatments. However, trial timelines are severely delayed with minimal success due to a multitude of factors, including imperfect trial site selection, cohort recruitment challenges, lack of efficacy, absence of reliable biomarkers, etc. Each of these factors possesses a unique computational challenge, such as data management, trial simulations, statistical analyses, and trial optimization. Recent advancements in quantum computing offer a promising opportunity to overcome these hurdles. In this opinion we uniquely explore the application of quantum optimization and quantum machine learning (QML) to the design and execution of clinical trials. We examine the current capabilities and limitations of quantum computing and outline its potential to streamline clinical trials.

Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment

By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on normalized advantage function reinforcement learning. It aims to boost the body's immune response against cancer cell proliferation. This control approach is applied to provide a combination of both chemotherapy and anti-angiogenic drugs for the first time without the need for complex, predefined mathematical models. It employs a model-free reinforcement learning technique that adaptively adjusts to individual patients to determine optimal drug administration with minimum injection rates. In this regard, a comprehensive and realistic simulation and training environment is employed, with the concentrations of normal cells, cancer cells, and endothelial cells, as well as the levels of chemotherapy and anti-angiogenic agents, as state variables. Furthermore, high levels of disturbances are considered in the simulation to investigate the robustness of the proposed method against probable uncertainties in the treatment process or patient parameters. A practical reward function has also been devised in alignment with medical objectives to ensure effective and safe treatment outcomes. The results demonstrate robustness and superior performance compared to the existing methods. Simulations show that the proposed approach is a dependable strategy for effectively reducing the concentration of cancer cells in the shortest duration using minimal doses of chemotherapy and anti-angiogenic drugs.

The 2022 PSOGI International Consensus on HIPEC Regimens for Peritoneal Malignancies: HIPEC Technologies

This manuscript reports the results of an international consensus on technologies of hyperthermic intraperitoneal perioperative chemotherapy (HIPEC) performed with the following goals: To provide recommendations for the technological parameters to perform HIPEC. To identify the role of heat and its application forms in treating peritoneal metastases. To provide recommendations regarding the correct dosimetry of intraperitoneal chemotherapy drugs and their carrier solutions. To identify for each intraperitoneal chemotherapy regimen the best dosimetry and fractionation. To identify areas of future research pertaining to HIPEC technology and regimens. This consensus was performed by the Delphi technique and comprised two rounds of voting. In total, 96 of 102 eligible panelists replied to both Delphi rounds (94.1%) with a consensus of 39/51 questions on HIPEC technical aspects. Among the recommendations that met with the strongest consensus were those concerning the dose of HIPEC drug established in mg/m2, a target temperature of at least 42°C, and the use of at least three temperature probes to pursue hyperthermia. Ninety minutes as the ideal HIPEC duration seemed to make consensus. These results should be considered when designing new clinical trials in patients with peritoneal surface malignancies.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Recommendations for clinical trial design in acute kidney injury from the 31st acute disease quality initiative consensus conference. A consensus statement

PURPOSE

Novel interventions for the prevention or treatment of acute kidney injury (AKI) are currently lacking. To facilitate the evaluation and adoption of new treatments, the use of the most appropriate design and endpoints for clinical trials in AKI is critical and yet there is little consensus regarding these issues. We aimed to develop recommendations on endpoints and trial design for studies of AKI prevention and treatment interventions based on existing data and expert consensus.

METHODS

At the 31st Acute Disease Quality Initiative (ADQI) meeting, international experts in critical care, nephrology, involving adults and pediatrics, biostatistics and people with lived experience (PWLE) were assembled. We focused on four main areas: (1) patient enrichment strategies, (2) prevention and attenuation studies, (3) treatment studies, and (4) innovative trial designs of studies other than traditional (parallel arm or cluster) randomized controlled trials. Using a modified Delphi process, recommendations and consensus statements were developed based on existing data, with > 90% agreement among panel members required for final adoption.

RESULTS

The panel developed 12 consensus statements for clinical trial endpoints, application of enrichment strategies where appropriate, and inclusion of PWLE to inform trial designs. Innovative trial designs were also considered.

CONCLUSION

The current lack of specific therapy for prevention or treatment of AKI demands refinement of future clinical trial design. Here we report the consensus findings of the 31st ADQI group meeting which has attempted to address these issues including the use of predictive and prognostic enrichment strategies to enable appropriate patient selection.

Adaptive Multiple Comparisons With the Best

Subset selection methods aim to choose a nonempty subset of populations including a best population with some prespecified probability. An example application involves location parameters that quantify yields in agriculture to select the best wheat variety. This is quite different from variable selection problems, for instance, in regression. Unfortunately, subset selection methods can become very conservative when the parameter configuration is not least favorable. This will lead to a selection of many non-best populations, making the set of selected populations less informative. To solve this issue, we propose less conservative adaptive approaches based on estimating the number of best populations. We also discuss variants of our adaptive approaches that are applicable when the sample sizes and/or variances differ between populations. Using simulations, we show that our methods yield a desirable performance. As an illustration of potential gains, we apply them to two real datasets, one on the yield of wheat varieties and the other obtained via genome sequencing of repeated samples.

Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities

Artificial intelligence tools promise transformative impacts in drug development. Regulatory agencies face challenges in integrating AI while ensuring reliability and safety in clinical trial approvals, drug marketing authorizations, and post-market surveillance. Incorporating these technologies into the existing regulatory framework and agency practices poses notable challenges, particularly in evaluating the data and models employed for these purposes. Rapid adaptation of regulations and internal processes is essential for agencies to keep pace with innovation, though achieving this requires collective stakeholder collaboration. This article thus delves into the need for adaptations of regulations throughout the drug development lifecycle, as well as the utilization of AI within internal processes of medicine agencies.

Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials

The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.

Drug development and evidence for lung cancer targeted therapy in Eastern Asia

The development of targeted drugs in the Eastern Asia region is going through a flourishing stage. With the continuous advancement of technology and medical research, biotechnology companies and research institutions in the region have made significant progress in cancer field. The Eastern Asian region not only actively participates in clinical trials, but is also committed to developing personalized medical plans to meet the diverse genotypes and phenotypes of patients. The governments and enterprises are increasingly valuing innovation, strengthening international cooperation, and promoting drug development. This paper summarizes the development of genetic testing technology, targeted drugs approval, ongoing promising clinical trials in the field of lung cancer and the important progress made by governments in the Eastern Asian region, and proposed key factors that will contribute to the promising future prospects in the region. The targeted drug market in the Eastern Asian region is expected to drive the medical field forward.

Efficacy of a synbiotic in the management of adults with Attention-Deficit and Hyperactivity Disorder and/or Borderline Personality Disorder and high levels of irritability: Results from a multicenter, randomized, placebo-controlled, "basket" trial

Irritability worsens prognosis and increases mortality in individuals with Attention-Deficit and Hyperactivity Disorder (ADHD) and/or Borderline Personality Disorder (BPD). However, treatment options are still insufficient. The aim of this randomized, double blind, placebo-controlled study was to investigate the superiority of a synbiotic over placebo in the management of adults with ADHD and/or BPD and high levels of irritability. The study was conducted between February 2019 and October 2020 at three European clinical centers located in Hungary, Spain and Germany. Included were patients aged 18-65 years old diagnosed with ADHD and/or BPD and high levels of irritability (i.e., an Affectivity Reactivity Index (ARI-S) ≥ 5, plus a Clinical Global Impression-Severity Scale (CGI-S) score ≥ 4). Subjects were randomized 1(synbiotic):1(placebo); the agent was administered each day, for 10 consecutive weeks. The primary outcome measure was end-of-treatment response (i.e., a reduction ≥ 30 % in the ARI-S total score compared to baseline, plus a Clinical Global Impression-Improvement (CGI-I) total score of < 3 (very much, or much improved) at week 10). Between-treatment differences in secondary outcomes, as well as safety were also investigated. Of the 231 included participants, 180 (90:90) were randomized and included in the intention-to-treat-analyses. Of these, 117 (65 %) were females, the mean age was 38 years, ADHD was diagnosed in 113 (63 %), BPD in 44 (24 %), both in 23 (13 %). The synbiotic was well tolerated. At week 10, patients allocated to the synbiotic experienced a significantly higher response rate compared to those allocated to placebo (OR: 0.2, 95 % CI:0.1 to 0.7; P = 0.01). These findings suggest that that (add-on) treatment with a synbiotic may be associated with a clinically meaningful improvement in irritability in, at least, a subgroup of adults with ADHD and/or BPD. A superiority of the synbiotic over placebo in the management of emotional dysregulation (-3.6, 95 % CI:-6.8 to -0.3; P = 0.03), emotional symptoms (-0.6, 95 % CI:-1.2 to -0.05; P = 0.03), inattention (-1.8, 95 % CI: -3.2 to -0.4; P = 0.01), functioning (-2.7, 95 % CI: -5.2 to -0.2; P = 0.03) and perceived stress levels (-0.6, 95 % CI: -1.2 to -0.05; P = 0.03) was also suggested. Higher baseline RANK-L protein levels were associated with a significantly lower response rate, but only in the synbiotic group (OR: 0.1, 95 % CI: -4.3 to - 0.3, P = 0.02). In the placebo group, higher IL-17A levels at baseline were significantly associated with a higher improvement in in particular, emotional dysregulation (P = 0.04), opening a door for new (targeted) drug intervention. However, larger prospective studies are warranted to confirm the findings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03495375.

Landscape of clinical drug development of ADCs used for the pharmacotherapy of cancers: an overview of clinical trial registry data from 2002 to 2022

BACKGROUND

To provide reference for clinical development of ADCs in the industry, we analyzed the landscape and characteristics of clinical trials about antibody-drug conjugates (ADCs).

METHOD

Clinical trials to study ADCs used for the pharmacotherapy of cancers initiated by the sponsor were searched in the Cite line Pharma Intelligence (Trialtrove database), and the landscape and characteristics of these clinical trials were analyzed from multiple perspectives, such as the number, phases, status, indications, and targets of the clinical trials.

RESULT

As of December 31, 2022, a total of 431 clinical trials have been initiated to study ADCs used for the pharmacotherapy of cancers, and the number of the last 10 years was 5.5 times as large as the first 11 years. These clinical trials involved 47 indications, including breast cancer, lymphoma (lymphoma, non-Hodgkin's and lymphoma, Hodgkin's), unspecified solid tumor, bladder cancer and lung cancer (lung, non-small cell cancer and lung, small cell cancer). As for each of these five indications, 50 + clinical trials have been carried out, accounting for as high as 48.50% (454/936). ADCs involve 38 targets, which are relatively concentrated. Among them, ERBB2 (HER2) and TNFRSF8 (CD30) involve in 100 + registered clinical trials, and TNFRSF17 (BCMA), NECTIN4 and CD19 in 10 + trials. The clinical trials for these five targets account for 79.02% (354/448) of the total number. Up to 93.97% (405/431) of these clinical trials explored the correlation between biomarkers and efficacy. Up to 45.91% (292/636) of Lots (lines of treatment) applied in the clinical trials were the second line. Until December 31, 2022, 54.52% (235/431) of the clinical trials have been completed or terminated.

CONCLUSION

ADCs are a hotspot of research and development in oncology clinical trials, but the indications, targets, phases, and Lot that have been registered are seemingly relatively concentrated at present. This study provides a comprehensive analysis which can assist researchers/developer quickly grasp relevant knowledge to assess a product and also providing new clues and ideas for future research.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Why Is Wnt/β-Catenin Not Yet Targeted in Routine Cancer Care?

Despite significant progress in cancer prevention, screening, and treatment, the still limited number of therapeutic options is an obstacle towards increasing the cancer cure rate. In recent years, many efforts were put forth to develop therapeutics that selectively target different components of the oncogenic Wnt/β-catenin signaling pathway. These include small molecule inhibitors, antibodies, and more recently, gene-based approaches. Although some of them showed promising outcomes in clinical trials, the Wnt/β-catenin pathway is still not targeted in routine clinical practice for cancer management. As for most anticancer treatments, a critical limitation to the use of Wnt/β-catenin inhibitors is their therapeutic index, i.e., the difficulty of combining effective anticancer activity with acceptable toxicity. Protecting healthy tissues from the effects of Wnt/β-catenin inhibitors is a major issue due to the vital role of the Wnt/β-catenin signaling pathway in adult tissue homeostasis and regeneration. In this review, we provide an up-to-date summary of clinical trials on Wnt/β-catenin pathway inhibitors, examine their anti-tumor activity and associated adverse events, and explore strategies under development to improve the benefit/risk profile of this therapeutic approach.

Perspectives and challenges of small molecule inhibitor therapy for FLT3-mutated acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous clonal disease characterized overall by an aggressive clinical course. The underlying genetic abnormalities present in leukemic cells contribute significantly to the AML phenotype. Mutations in FMS-like tyrosine kinase 3 (FLT3) are one of the most common genetic abnormalities identified in AML, and the presence of these mutations strongly influences disease presentation and negatively impacts prognosis. Since mutations in FLT3 were identified in AML, they have been recognized as a valid therapeutic target resulting in decades of research to develop effective small molecule inhibitor treatment that could improve outcome for these patients. Despite the approval of several FLT3 inhibitors over the last couple of years, the treatment of patients with FLT3-mutated AML remains challenging and many questions still need to be addressed. This review will provide an up-to-date overview of our current understanding of FLT3-mutated AML and discuss what the current status is of the available FLT3 inhibitors for the day-to-day management of this aggressive disease.

Examining external control arms in oncology: A scoping review of applications to date

OBJECTIVES

Randomized controlled trials (RCTs) are the gold standard for evaluating the comparative efficacy and safety of new cancer therapies. However, enrolling patients in control arms of clinical trials can be challenging for rare cancers, particularly in the context of precision oncology and targeted therapies. External Control Arms (ECAs) are a potential solution to address these challenges in clinical research design. We conducted a scoping review to explore the use of ECAs in oncology.

METHODS

We systematically searched four databases, namely MEDLINE, EMBASE, Web of Science, and Scopus. We screened titles, abstracts, and full texts for eligible articles focusing on patients undergoing therapy for cancer, employing ECAs, and reporting clinical outcomes.

RESULTS

Of the 629 articles screened, 23 were included in this review. The earliest included studies were published in 1996, while most studies were published in the past 5 years. 44% (10/23) of ECAs were employed in blood-related cancer studies. Geographically, 30% (7/23) of studies were conducted in the United States, 22% (5/23) in Japan, and 9% (2/23) in South Korea. The primary data sources used to construct the ECAs involved pooled data from previous trials (35%, 8/23), administrative health databases (17%, 4/23) and electronic medical records (17%, 4/23). While 52% (12/23) of the studies employed methods to align treatment and ECAs characteristics, 48% (11/23) lacked explicit strategies.

CONCLUSION

ECAs offer a valuable approach in oncology research, particularly when alternative designs are not feasible. However, careful methodological planning and detailed reporting are essential for meaningful and reliable results.

A novel prognostic signature related to programmed cell death in osteosarcoma

Background

Osteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.

Method

We retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.

Result

In our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.

Conclusion

The five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

Tumor-Agnostic Therapy-The Final Step Forward in the Cure for Human Neoplasms?

Cancer accounted for 10 million deaths in 2020, nearly one in every six deaths annually. Despite advancements, the contemporary clinical management of human neoplasms faces a number of challenges. Surgical removal of tumor tissues is often not possible technically, while radiation and chemotherapy pose the risk of damaging healthy cells, tissues, and organs, presenting complex clinical challenges. These require a paradigm shift in developing new therapeutic modalities moving towards a more personalized and targeted approach. The tumor-agnostic philosophy, one of these new modalities, focuses on characteristic molecular signatures of transformed cells independently of their traditional histopathological classification. These include commonly occurring DNA aberrations in cancer cells, shared metabolic features of their homeostasis or immune evasion measures of the tumor tissues. The first dedicated, FDA-approved tumor-agnostic agent's profound progression-free survival of 78% in mismatch repair-deficient colorectal cancer paved the way for the accelerated FDA approvals of novel tumor-agnostic therapeutic compounds. Here, we review the historical background, current status, and future perspectives of this new era of clinical oncology.

Nanoengineering Solutions for Cancer Therapy: Bridging the Gap between Clinical Practice and Translational Research

Nanoengineering has emerged as a progressive method in cancer treatment, offering precise and targeted delivery of therapeutic agents while concurrently reducing overall toxicity. This scholarly article delves into the innovative strategies and advancements in nanoengineering that bridge the gap between clinical practice and research in the field of cancer treatment. Various nanoengineered platforms such as nanoparticles, liposomes, and dendrimers are scrutinized for their capacity to encapsulate drugs, augment drug efficacy, and enhance pharmacokinetics. Moreover, the article investigates research breakthroughs that drive the progression and enhancement of nanoengineered remedies, encompassing the identification of biomarkers, establishment of preclinical models, and advancement of biomaterials, all of which are imperative for translating laboratory findings into practical medical interventions. Furthermore, the integration of nanotechnology with imaging modalities, which amplify cancer detection, treatment monitoring, and response assessment, is thoroughly examined. Finally, the obstacles and prospective directions in nanoengineering, including regulatory challenges and issues related to scalability, are examined. This underscores the significance of fostering collaboration among various entities in order to efficiently translate nanoengineered interventions into enhanced cancer therapies and patient management.

Evolving assessment pathways for precision oncology medicines to improve patient access: a tumor-agnostic lens

BACKGROUND

Genomic and molecular alterations are increasingly important in cancer diagnosis, and scientific advances are opening new treatment avenues. Precision oncology (PO) uses a patient's genomic profile to determine optimal treatment, promising fewer side effects and higher success rates. Within PO, tumor-agnostic (TA) therapies target genomic alterations irrespective of tumor location. However, traditional value frameworks and approval pathways pose challenges which may limit patient access to PO therapies.

OBJECTIVES

This study describes challenges in assessing PO and TA medicines, explores possible solutions, and provides actionable recommendations to facilitate an iterative life-cycle assessment of these medicines.

METHODS

After reviewing the published literature, we obtained insights from key stakeholders and European experts across a range of disciplines, through individual interviews and an industry workshop. The research was guided and refined by an international expert committee through 2 sounding board meetings.

RESULTS

The current challenges faced by PO and TA medicines are multiple and can be demonstrated through real-world examples of the current barriers and opportunities. A life-cycle approach to assessment should be taken, including key actions at the early stages of evidence generation, regulatory and reimbursement stage, as well as payment and adoption solutions that make use of the evolving evidence base. Working toward these solutions to maximize PO medicine value is a shared responsibility and stands to benefit all stakeholders.

CONCLUSIONS

Our call to action is to expand access to comprehensive genomic testing, foster a learning health care system, enable fast and equitable access to cost-effective treatments, and ultimately improve health outcomes.

Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position

Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.

Disparities in clinical trial enrollment among patients with gastrointestinal cancer relative to minority-serving and safety-netting hospitals

BACKGROUND

For results to be generalizable to all patients with cancer, clinical trials need to include a diverse patient demographic that is representative of the general population. We sought to characterize the effect of receiving care at a minority-serving hospital (MSH) and/or safety-net hospital on clinical trial enrollment among patients with gastrointestinal (GI) malignancies.

METHODS

Adult patients with GI cancer who underwent oncologic surgery and were enrolled in institutional-/National Cancer Institute-funded clinical trials between 2012 and 2019 were identified in the National Cancer Database. Multivariable regression was used to assess the relationship between MSH and safety-net status relative to clinical trial enrollment.

RESULTS

Among 1,112,594 patients, 994,598 (89.4%) were treated at a non-MSH, whereas 117,996 (10.6%) were treated at an MSH. Only 1857 patients (0.2%) were enrolled in a clinical trial; most patients received care at a non-MSH (1794 [96.6%]). On multivariable analysis, the odds of enrollment in a clinical trial were markedly lower among patients treated at an MSH vs non-MSH (odds ratio [OR], 0.32; 95% CI, 0.22-0.46). In addition, even after controlling for receipt of care at MSH, Black patients remained at lower odds of enrollment in a clinical trial than White patients (OR, 0.57; 95% CI, 0.45-0.73; both P < .05).

CONCLUSION

Overall, clinical trial participation among patients with GI cancer was extremely low. Patients treated at an MSH and high safety-net burden hospitals and Black individuals were much less likely to be enrolled in a clinical trial. Efforts should be made to improve trial enrollment and address disparities in trial representation.

Anticancer drugs: How to select small molecule combinations?

Small molecules are at the forefront of anticancer therapies. Successive treatments with single molecules incur drug resistance, calling for combination. Here, we explore the tough choices oncologists face - not just which drugs to use but also the best treatment plans, based on factors such as target proteins, pathways, and gene expression. We consider the reality of cancer's disruption of normal cellular processes, highlighting why it's crucial to understand the ins and outs of current treatment methods. The discussion on using combination drug therapies to target multiple pathways sheds light on a promising approach while also acknowledging the hurdles that come with it, such as dealing with pathway crosstalk. We review options and provide examples and the mechanistic basis, altogether providing the first comprehensive guide to combinatorial therapy selection.

Promise and Perils of Precision Oncology for Patients With Pediatric and Young Adult Sarcomas

The completion of multiple national pediatric precision oncology platform trials and the incorporation of standardized molecular profiling into the diagnostic care of pediatric and young adult patients with sarcomas have proven the feasibility and potential of the approach. In this work, we explore the current state of the art of precision oncology for pediatric and young adults with sarcoma. We highlight important lessons learned and the challenges that should be addressed in the next generation of trials. The chapter outlines current efforts to improve standardization of molecular assays, harmonization of data collection, and novel molecular tools such as cell-free DNA analyses. Finally, we discuss the impacts and psychosocial outcomes experienced by patients and communication strategies for providers.

Targeting PI3K/AKT/mTOR signaling to overcome drug resistance in cancer

This review comprehensively explores the challenge of drug resistance in cancer by focusing on the pivotal PI3K/AKT/mTOR pathway, elucidating its role in oncogenesis and resistance mechanisms across various cancer types. It meticulously examines the diverse mechanisms underlying resistance, including genetic mutations, feedback loops, and microenvironmental factors, while also discussing the associated resistance patterns. Evaluating current therapeutic strategies targeting this pathway, the article highlights the hurdles encountered in drug development and clinical trials. Innovative approaches to overcome resistance, such as combination therapies and precision medicine, are critically analyzed, alongside discussions on emerging therapies like immunotherapy and molecularly targeted agents. Overall, this comprehensive review not only sheds light on the complexities of resistance in cancer but also provides a roadmap for advancing cancer treatment.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

Precision medicine in Asia enhanced by next-generation sequencing: Implications for Thailand through a scoping review and interview study

Next-generation sequencing (NGS) significantly enhances precision medicine (PM) by offering personalized approaches to diagnosis, treatment, and prevention of unmet medical needs. Little is known about the current situation of PM in Asia. Thus, we aimed to conduct an overview of the progress and gaps in PM in Asia and enrich it with in-depth insight into the possibilities of future PM in Thailand. This scoping review focused on Asian countries starting with non-cancer studies, including rare and undiagnosed diseases (RUDs), non-communicable diseases (NCDs), infectious diseases (IDs), and pharmacogenomics, with a focus on NGS. Subsequent in-depth interviews with experts in Thailand were performed, and a thematic analysis served as the main qualitative methodology. Out of 2898 searched articles, 387 studies were included after the review. Although most of the studies focused on cancer, 89 (23.0%) studies were related to RUDs (17.1%), NCDs (2.8%), IDs (1.8%), and pharmacogenomics (1.3%). Apart from medicine and related sciences, the studies were mostly composed of PM (61.8%), followed by genetics medicine and bioinformatics. Interestingly, 28% of articles were conducted exclusively within the fields of medicine and related sciences, emphasizing interdisciplinary integration. The experts emphasized the need for sustainability-driven political will, nurturing collaboration, reinforcing computational infrastructure, and expanding the bioinformatic workforce. In Asia, developments of NGS have made remarkable progress in PM. Thailand has extended PM beyond cancer and focused on clinical implementation. We summarized the PM challenges, including equity and efficiency targeting, guided research funding, sufficient sample size, integrated collaboration, computational infrastructure, and sufficient trained human resources.

Decoding the neurotoxic effects of propofol: insights into the RARα-Snhg1-Bdnf regulatory cascade

The potential neurotoxic effects of propofol, an extensively utilized anesthetic, underline the urgency to comprehend its influence on neuronal health. Insights into the role of the retinoic acid receptor-α, small nucleolar RNA host gene 1, and brain-derived neurotrophic factor (RARα-Snhg1-Bdnf) network can offer significant advancements in minimizing these effects. The study targets the exploration of the RARα and Snhg1 regulatory network's influence on Bdnf expression in the realm of propofol-induced neurotoxicity. Harnessing the Gene Expression Omnibus (GEO) database and utilizing JASPAR and RNA-Protein Interaction Prediction (RPISeq) database for projections, the study embarks on an in-depth analysis employing both in vitro and in vivo models. The findings draw a clear link between propofol-induced neurotoxicity and the amplification of RAR signaling pathways, impacting hippocampal development and apoptosis and leading to increased RARα and Snhg1 and decreased Bdnf. Propofol is inferred to accentuate neurotoxicity by heightening RARα and Snhg1 interactions, culminating in Bdnf suppression.NEW & NOTEWORTHY This study aimed to decode propofol's neurotoxic effects on the regulatory cascade, provide insights into the RARα-Snhg1-Bdnf interaction, apply extensive validation techniques, provide a detailed analysis and exploration of propofol's neurotoxicity, and offer a comprehensive approach to understanding molecular interactions.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

[Precision oncology and molecular tumor boards]

Precision oncology is a field of personalized medicine in which tumor biology forms the basis for tailored treatments. The preferred approach currently applied in clinical practice is based on the concept of malignant tumors as genetic diseases that are caused by mutations in oncogenes and tumor suppressors. On the one hand, these can be targeted by molecular drugs, while on the other hand, next-generation sequencing allows for comprehensive analysis of all relevant aberrations, thus enabling the matching of appropriate treatments across entities based on molecular information. Rational molecular therapies are developed and annotated with supporting evidence by molecular tumor boards, which have been established at various academic centers in recent years. Advancing precision oncology to a new standard of care requires improved applicability of personalized molecular therapies and thorough scientific evaluation of precision oncology programs.

Precision Endpoints for Contemporary Precision Oncology Trials

SUMMARY

Traditional endpoints such as progression-free survival and overall survival do not fully capture the pharmacologic and pharmacodynamic effects of a therapeutic intervention. Incorporating mechanism-driven biomarkers and validated surrogate proximal endpoints can provide orthogonal readouts of anti-tumor activity and delineate the relative contribution of treatment components on an individual level, highlighting the limitation of solely relying on aggregated readouts from clinical trials to facilitate go/no-go decisions for precision therapies.