Since the outbreak of coronavirus disease 2019 (COVID-19) was first identified, the world has vehemently worked to develop treatments and vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at an unprecedented speed. Few of the repositioned drugs for COVID-19 have shown that they were efficacious and safe. In contrast, a couple of vaccines against SARS-CoV-2 will be ready for mass rollout early next year. Despite successful vaccine development for COVID-19, the world will face a whole new set of challenges including scale-up manufacturing, cold-chain logistics, long-term safety, and low vaccine acceptance. We highlighted the importance of knowledge sharing and collaboration to find innovative answers to these challenges and to prepare for newly emerging viruses.

Therapeutics and vaccines against the COVID-19 pandemic need to be developed rapidly and efficiently, given its severity. To maximize the efficiency and productivity of drug development, the world has adopted disruptive technologies and approaches in various drug development areas. Telehealth, characterized by the heavy use of digital technologies; drug repositioning strategies, aided by computational breakthroughs; and data tracking tool hubs, enabling real-time information sharing, have received much attention. Moreover, drug developers have engaged in open innovation by establishing various types of collaborations, many of which have been carried out across nations and enterprises. Finally, regulatory agencies have attempted to operate on a more flexible review basis than before. Although such disruptive approaches have partly reshaped drug development practices, issues and challenges remain before the completion of this paradigm shift in conventional drug development practices for the post-pandemic era. In this review, we have highlighted the role of a collaborative community of experts in order to figure out how disruptive technologies can be fully integrated into the current drug development practices and improve drug development efficiency for the post-pandemic era.

Background: Rising costs of cancer treatments challenge even areas with universal health coverage. There's a need to assess current medical care utilization trends among patients with cancer to guide public health policy, resource allocation, and set informed healthcare goals.Methods: We analyzed the latest trends in medical care utilization by cancer patients in four areas—drugs, radiation therapy (RT), surgery, and diagnostic procedures—using clinical databases extracted from electronic medical records of a tertiary hospital in Korea between 2014 and 2019. Compound adjusted growth rates (CAGR) were computed to capture the annual growth over the study period.Results: A total of 74,285 cancer patients were identified, with 40.3% (29,962), 14.2% (10,577), 31.1% (23,066), and 92.6% (68,849) of patients having received at least one anticancer agent, RT, surgery, and diagnostic procedure, respectively, over the period. We observed a 1.7‐fold increase in the use of targeted · immune‐oncology agents (from 6.8% to 11.6%) and a 21‐fold increase (from 3.0% in 2014 to 65.7%) in intensity‐modulated RT (IMRT) use over the period. In contrast, we observed a continuous decrease in the proportion of patients who underwent surgical treatment from 12.2% in 2014 to 10.9% in 2019. This decrease was particularly noticeable in patients with colon cancer (from 28.5% to 24.2%) and liver cancer (from 4.1% to 2.9%).Conclusion: From 2014 to 2019, there was a significant rise in the use of targeted · immune‐oncology agents and IMRT, alongside a decline in surgeries. While targeted · immune‐oncology agents and IMRT may offer promising outcomes, their financial impact and potential for overuse necessitate careful oversight and long‐term cost‐effectiveness studies.

Background: Real-world data (RWD)–based pediatric acute lymphoblastic leukemia (ALL) registries often face scalability and reliability limits owing to manual data entry and inter-institutional heterogeneity. We evaluated the feasibility of building an automated, high-quality registry using electronic medical records (EMR) and clinical data warehouses (CDWs) to quantitatively assess the Pediatric ALL Automated Registry (PeARL) for (1) automatic extractability (ADE) (2), data quality, and (3) multicenter applicability. Methods: In this study, we used CDWs to identify patients aged < 18 years with ALL (ICD-10 C91.0) who visited Seoul National University Hospital (SNUH) and the Catholic Medical Center (CMC). between 1990 and 2023. An automated extraction pipeline applied standard mapping, multivariate transformations, and rule-based natural language processing (NLP). Key variables were selected using the Medical Information Standards for Hematologic Cancer. Data quality was evaluated based on 228 rules across five dimensions (completeness, validity, accuracy, uniqueness, and consistency) based on DQ4HEALTH; error rates, defined as the proportion of data elements violating these rules, were calculated before and after quality management. Results: Overall, 1,609 patients were included (CMC 946; SNUH 663). ADE for key variables was 89.7% at SNUH and 75.0% at CMC; most automations were single-field transformations (SNUH 61.8%; CMC 84.7%), with multivariate transformations and rule-based NLP addressing complex elements. Initial overall error rates were 1.858% (SNUH) and 0.129% (CMC), decreasing to 0.001% at both institutions after quality processes. Differences in CDW structure required additional preprocessing for laboratory and transplant variables, but harmonization was achieved via standardized table specifications and cross-site review. Conclusions: PeARL integrated standardized mapping, multivariate transformations, and rule-based NLP to enable large-scale automation of a pediatric ALL registry, achieving a 0.001% overall error rate under DQ4HEALTH-based quality management. This clinically guided, standardized framework enables reproducible implementation and scalable automation of pediatric ALL registry construction, supporting multicenter research and the generation of regulatory-grade real-world evidence. Clinical trial number: Not applicable.

Our study aims to improve the prediction performance of machine learning (ML) models by addressing false records (i.e., false positive, false negative, or missingness) in binary categorical variables in electronic medical records (EMRs) using propensity score (PS). This study used the EMRs of patients with type 2 diabetes mellitus (T2DM) treated with basal insulin at a tertiary university hospital in South Korea. We expanded the definition of PS into the probability of having a record for a binary variable given covariates. We calculated PS for the binary categorical variables in their EMRs and developed PS datasets. By utilizing various ML algorithms, we developed and validated ACS prediction models on 80% and 20% of the dataset, respectively. We evaluated model performance using accuracy, recall, precision, F1 score, and AUROC. Additionally, the Shapley Additive Explanation (SHAP) method was used to identify important clinical predictors of ACS. The study included 9,338 patients (with an average age of 60.2 years and 56.6% of whom were male) over 10,184 treatment periods. The most prevalent comorbidities were hypertension (31.5%) and dyslipidemia (28.9%). Notably, 6.9% experienced ACS during their insulin treatment. The ML models trained on PS datasets generally outperformed the models trained on raw datasets. The results of SHAP analysis showed that older age, higher baseline weight, higher baseline glucose, history of antithrombotic therapy, history of chest pain, and indicators of T2DM progression (e.g., senile cataract) were important ACS risk factors. We have developed an ACS prediction model with an improved performance and higher reliance on clinical predictors that are in alignment with current medical understanding.

Pivotal clinical trials (PCTs) are often conducted across multiple nations and regions to achieve geographic, racial, or ethnic diversity. However, the diversity of PCT participants remains inadequate. PCTs for new drugs approved by the US Food and Drug Administration in 2012–2021 and their primary publications (PPub) were identified from the label and review documents. The country information about the PCT investigators and PPub authors were extracted from the PPubs or clinicaltrials.gov. The racial/ethnic diversity of PCT participants as reported in PPubs was assessed using the Diversity Index (DI). For 429 new drugs, 734 PCTs and 718 PPubs were identified. North America and Western Europe (NAWE) contributed the largest proportion of PCT investigators, PPub authors, and lead authors (62.9%, 81.7%, and 90.9%, respectively). Of 521 PPubs that reported the racial/ethnic distribution of PCT participants, the median DI was low at 0.33 (interquartile range 0.18–0.46) and only 16.3% PPubs had a DI >0.5. The number of PPub authors in non‐NAWE and the number of countries of PPub authors in non‐NAWE was significantly associated with the racial/ethnic diversity of PCT participants (OR 1.17 [95% CI 1.08–1.26] and OR 1.25 [1.08–1.46], respectively). Evidence generation for the regulatory approval of new drugs has been predominantly centered in NAWE. The dominance is even more pronounced in the authorship of PPubs. To improve the racial/ethnic diversity of PCT participants, investigators from non‐NAWE countries should be encouraged to play more leadership roles, thereby increasing their likelihood of serving as authors in PPub.