FDA Approval Summary: Palbociclib for Male Patients with Metastatic Breast Cancer

Importance of Health Economics and Outcomes Research in the Product Lifecycle

Health economics and outcomes research (HEOR) has become an integral part of healthcare systems, through its ability to authentically demonstrate the value of the product. HEOR provides healthcare stakeholders with important insights to make informed decisions regarding healthcare delivery. This review aims to highlight the pivotal role of HEOR across the product lifecycle and the value of integrating HEOR activities during the various phases of drug development. Pharmaceutical companies are increasingly realizing that the integration of HEOR activities from early phases of product development through product launch, also during the postmarketing phase, to generate real-world evidence (RWE) can be crucial for their product's continued commercial success. HEOR helps validate the value of a pharmaceutical product, enabling its success in distinct regulatory and health technology assessment (HTA) landscapes across varied geographies. Regardless of several challenges in data collection and analysis, technological advancements facilitate opportunities to improve the value of HEOR. With rising demands for robust clinical evidence by global regulators and economic evidence by HTA agencies and payers, HEOR will become even more crucial in establishing long-lasting value of a pharmaceutical product for all stakeholders, including regulators, patients, prescribers, and payers.

Use of Real-World Data and Real-World Evidence in Rare Disease Drug Development: A Statistical Perspective

Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challenges in rare disease drug development (see the accompanying paper), the Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section reviews the roles of RWD and RWE in clinical trials for drugs treating rare diseases. This paper summarizes relevant guidance documents and frameworks by selected regulatory agencies and the current practice on the use of RWD and RWE in natural history studies and the design, conduct, and analysis of rare disease clinical trials. A targeted learning roadmap for rare disease trials is described, followed by case studies on the use of RWD and RWE to support a natural history study and marketing applications in various settings.

Implementing Accuracy, Completeness, and Traceability for Data Reliability

Importance

While it is well known that data quality underlies evidence validity, the measurement and impacts of data reliability are less well understood. The need has been highlighted in the 21st Century Cures Act of 2016 and US Food and Drug Administration (FDA) Real-World Evidence Program framework in 2018, draft guidance in 2021 and final guidance in 2024. Timely visibility into implementation may be provided by the Transforming Real-World Evidence With Unstructured and Structured Data to Advance Tailored Therapy (TRUST) study, a Verantos Inc-led FDA-funded demonstration project to explore data quality and inform regulatory decision-making.

Objective

To report early learnings from the TRUST study on distilling data reliability to practice including developing a practical approach to quantify accuracy, completeness, and traceability of real-world data (routinely collected patient health data) and comparing traditional to advanced data and technologies on these dimensions.

Design, Setting, and Participants

This quality improvement study was performed using data from 58 hospitals and more than 1180 associated outpatient clinics from academic and community settings in the US. Participants included patients with asthma treated between January 1, 2014, and December 31, 2022. Data were analyzed from January 1 to June 30, 2024.

Exposures

The traditional approach used medical and pharmacy claims as source documentation. The advanced approach used medical and pharmacy claims, electronic health records with unstructured data extracted using artificial intelligence methods, and mortality registry data.

Main Outcomes and Measures

Accuracy was assessed using the F1 score. Completeness was estimated as a weighted mean of available data sources during each calendar year under study for each patient. Traceability was estimated as the proportion of data elements identified in clinical source documentation.

Results

In total, 120 616 patients met the minimum data requirements (mean [SD] age, 43.2 [18.5] years; 41 011 male [34.0%]). For accuracy, traditional approaches had F1 scores of 59.5% and advanced approaches had scores of 93.4%. For completeness, traditional approaches yielded mean scores of 46.1% (95% CI, 38.2%-54.0%); advanced approaches, 96.6% (95% CI, 85.8%-1.1%). For traceability, traditional approaches had 11.5% (95% CI, 11.4%-11.5%) and advanced approaches had 77.3% (95% CI, 77.3%-77.3%) of data elements traceable to clinical source data.

Conclusions and Relevance

In this study, practical implementation of data reliability measurement is described. Findings suggest the potential of using multiple data sources and applying advanced methods to increase real-world data reliability. The inclusion of data reliability standards when generating evidence from these sources has the potential to strengthen support for the use of real-world evidence in the prescription, reimbursement, and approval of medications.

Radiogenomic Landscape of Metastatic Endocrine-Positive Breast Cancer Resistant to Aromatase Inhibitors

Breast cancer poses a significant global health challenge and includes various subtypes, such as endocrine-positive, HER2-positive, and triple-negative. Endocrine-positive breast cancer, characterized by estrogen and progesterone receptors, is commonly treated with aromatase inhibitors. However, resistance to these inhibitors can hinder patient outcomes due to genetic and epigenetic alterations, mutations in the estrogen receptor 1 gene, and changes in signaling pathways. Radiogenomics combines imaging techniques like MRI and CT scans with genomic profiling methods to identify radiographic biomarkers associated with resistance. This approach enhances our understanding of resistance mechanisms and metastasis patterns, linking them to specific genomic profiles and common metastasis sites like the bone and brain. By integrating radiogenomic data, personalized treatment strategies can be developed, improving predictive and prognostic capabilities. Advancements in imaging and genomic technologies offer promising avenues for enhancing radiogenomic research. A thorough understanding of resistance mechanisms is crucial for developing effective treatment strategies, making radiogenomics a valuable integrative approach in personalized medicine that aims to improve clinical outcomes for patients with metastatic endocrine-positive breast cancer.

Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review

BACKGROUND

Electronic health records (EHRs) facilitate the accessibility and sharing of patient data among various health care providers, contributing to more coordinated and efficient care.

OBJECTIVE

This study aimed to summarize the evolution of secondary use of EHRs and their interoperability in medical research over the past 25 years.

METHODS

We conducted an extensive literature search in the PubMed, Scopus, and Web of Science databases using the keywords Electronic health record and Electronic medical record in the title or abstract and Medical research in all fields from 2000 to 2024. Specific terms were applied to different time periods.

RESULTS

The review yielded 2212 studies, all of which were then screened and processed in a structured manner. Of these 2212 studies, 2102 (93.03%) were included in the review analysis, of which 1079 (51.33%) studies were from 2000 to 2009, 582 (27.69%) were from 2010 to 2019, 251 (11.94%) were from 2020 to 2023, and 190 (9.04%) were from 2024.

CONCLUSIONS

The evolution of EHRs marks an important milestone in health care's journey toward integrating technology and medicine. From early documentation practices to the sophisticated use of artificial intelligence and big data analytics today, EHRs have become central to improving patient care, enhancing public health surveillance, and advancing medical research.

Real-world data: bridging the gap between clinical trials and practice

Real-world data (RWD) are rapidly emerging sources of information for patients, clinicians and regulators. While randomized controlled trials (RCTs) reduce bias and confounding through the randomization process and provide the highest quality of evidence regarding drug efficacy, RCTs may be impractical or unfeasible for rare diseases or disease subsets. And yet, studies attempting to replicate clinical trial results using observational datasets have failed. Given the inherent differences between observational data and clinical trial results, this discordance is not surprising. However, RWD may still have independent value as complementary tools to trial results. In this viewpoint, we explore the challenges of RWD and discuss key questions that clinicians, patients, and regulators will need to consider when faced with positive efficacy data from clinical trials, and negative effectiveness data from real world studies. Finally, we explore novel trial designs that might help bridge the gap from RCTs to RWD.

Real-World Evidence Application in Translational Medicine: Making Use of Prescription Claims to Inform Drug-Drug Interactions of a New Psoriasis Treatment

Patients with psoriasis often take multiple medications due to comorbidities, raising concerns about drug-drug interactions (DDIs) during the development of new medicines. DDI risk assessments of a new small molecule showed risks of CYP3A4 autoinduction and being a sensitive CYP3A4 substrate. We conducted a real-world evidence (RWE) claims analysis to assess the frequency of prescription claims for up to 12 months from the date of the initial psoriasis diagnosis for drugs that may interact with CYP3A4 substrates. We used 2013 to 2018 patient data from the US Merative MarketScan Research Database. Among patients diagnosed with psoriasis, less than 1% had a claim for a moderate/strong inducer, but up to 15% had a claim for moderate/strong inhibitor. Most prescriptions for CYP3A4 inhibitors or inducers included antibiotics and anticonvulsants. While CYP3A4 inducers were rarely used, those treated received more than >90 days treatment. Then, these RWE data were used to inform the early translational medicine strategy for the new investigational drug by strategically integrating DDI evaluations into a first-in-human healthy volunteer trial prior to studies in patients with psoriasis. The resulting DDI substudy showed that the investigational small molecule did not induce midazolam clearance but was sensitive to CYP3A inhibition, leading to the decision to exclude concomitant use of strong CYP3A4 inducers or inhibitors from clinical trials.

Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation

BACKGROUND

Clinical trials are vital for developing new therapies but can also delay drug development. Efficient trial data management, optimized trial protocol, and accurate patient identification are critical for reducing trial timelines. Natural language processing (NLP) has the potential to achieve these objectives.

OBJECTIVE

This study aims to assess the feasibility of using data-driven approaches to optimize clinical trial protocol design and identify eligible patients. This involves creating a comprehensive eligibility criteria knowledge base integrated within electronic health records using deep learning-based NLP techniques.

METHODS

We obtained data of 3281 industry-sponsored phase 2 or 3 interventional clinical trials recruiting patients with non-small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, and Crohn disease from ClinicalTrials.gov, spanning the period between 2013 and 2020. A customized bidirectional long short-term memory- and conditional random field-based NLP pipeline was used to extract all eligibility criteria attributes and convert hypernym concepts into computable hyponyms along with their corresponding values. To illustrate the simulation of clinical trial design for optimization purposes, we selected a subset of patients with non-small cell lung cancer (n=2775), curated from the Mount Sinai Health System, as a pilot study.

RESULTS

We manually annotated the clinical trial eligibility corpus (485/3281, 14.78% trials) and constructed an eligibility criteria-specific ontology. Our customized NLP pipeline, developed based on the eligibility criteria-specific ontology that we created through manual annotation, achieved high precision (0.91, range 0.67-1.00) and recall (0.79, range 0.50-1) scores, as well as a high F1-score (0.83, range 0.67-1), enabling the efficient extraction of granular criteria entities and relevant attributes from 3281 clinical trials. A standardized eligibility criteria knowledge base, compatible with electronic health records, was developed by transforming hypernym concepts into machine-interpretable hyponyms along with their corresponding values. In addition, an interface prototype demonstrated the practicality of leveraging real-world data for optimizing clinical trial protocols and identifying eligible patients.

CONCLUSIONS

Our customized NLP pipeline successfully generated a standardized eligibility criteria knowledge base by transforming hypernym criteria into machine-readable hyponyms along with their corresponding values. A prototype interface integrating real-world patient information allows us to assess the impact of each eligibility criterion on the number of patients eligible for the trial. Leveraging NLP and real-world data in a data-driven approach holds promise for streamlining the overall clinical trial process, optimizing processes, and improving efficiency in patient identification.

Cell cycle machinery in oncology: A comprehensive review of therapeutic targets

The cell cycle is tightly regulated to ensure controlled cell proliferation. Dysregulation of the cell cycle machinery is a hallmark of cancer that leads to unchecked growth. This review comprehensively analyzes key molecular regulators of the cell cycle and how they contribute to carcinogenesis when mutated or overexpressed. It focuses on cyclins, cyclin-dependent kinases (CDKs), CDK inhibitors, checkpoint kinases, and mitotic regulators as therapeutic targets. Promising strategies include CDK4/6 inhibitors like palbociclib, ribociclib, and abemaciclib for breast cancer treatment. Other possible targets include the anaphase-promoting complex/cyclosome (APC/C), Skp2, p21, and aurora kinase inhibitors. However, challenges with resistance have limited clinical successes so far. Future efforts should focus on combinatorial therapies, next-generation inhibitors, and biomarkers for patient selection. Targeting the cell cycle holds promise but further optimization is necessary to fully exploit it as an anti-cancer strategy across diverse malignancies.

Improving Clinical Registry Data Quality via Linkage With Survival Data From State-Based Population Registries

PURPOSE

Real-world data (RWD) collected on patients treated as part of routine clinical care form the basis of cancer clinical registries. Capturing accurate death data can be challenging, with inaccurate survival data potentially compromising the integrity of registry-based research. Here, we explore the utility of data linkage (DL) to state-based registries to enhance the capture of survival outcomes.

METHODS

We identified consecutive adult patients with brain tumors treated in the state of Victoria from the Brain Tumour Registry Australia: Innovation and Translation (BRAIN) database, who had no recorded date of death and no follow-up within the last 6 months. Full name and date of birth were used to match patients in the BRAIN registry with those in the Victorian Births, Deaths and Marriages (BDM) registry. Overall survival (OS) outcomes were compared pre- and post-DL.

RESULTS

Of the 7,346 clinical registry patients, 5,462 (74%) had no date of death and no follow-up recorded within the last 6 months. Of the 5,462 patients, 1,588 (29%) were matched with a date of death in BDM. Factors associated with an increased number of matches were poor prognosis tumors, older age, and social disadvantage. OS was significantly overestimated pre-DL compared with post-DL for the entire cohort (pre- v post-DL: hazard ratio, 1.43; P < .001; median, 29.9 months v 16.7 months) and for most individual tumor types. This finding was present independent of the tumor prognosis.

CONCLUSION

As revealed by linkage with BDM, a high proportion of patients in a brain cancer clinical registry had missing death data, contributed to by informative censoring, inflating OS calculations. DL to pertinent registries on an ongoing basis should be considered to ensure accurate reporting of survival data and interpretation of RWD outcomes.

When Will Real-World Data Fulfill Its Promise to Provide Timely Insights in Oncology?

Randomized trials provide high-quality, internally consistent data on selected clinical questions, but lack generalizability for the aging population who are most often diagnosed with cancer and have comorbid conditions that may affect the interpretation of treatment benefit. The need for high-quality, relevant, and timely data is greater than ever. Promising solutions lie in the collection and analysis of real-world data (RWD), which can potentially provide timely insights about the patient's course during and after initial treatment and the outcomes of important subgroups such as the elderly, rural populations, children, and patients with greater social health needs. However, to inform practice and policy, real-world evidence must be created from trustworthy and comprehensive sources of RWD; these may include pragmatic clinical trials, registries, prospective observational studies, electronic health records (EHRs), administrative claims, and digital technologies. There are unique challenges in oncology since key parameters (eg, cancer stage, biomarker status, genomic assays, imaging response, side effects, quality of life) are not recorded, siloed in inaccessible documents, or available only as free text or unstructured reports in the EHR. Advances in analytics, such as artificial intelligence, may greatly enhance the ability to obtain more granular information from EHRs and support integrated diagnostics; however, they will need to be validated purpose by purpose. We recommend a commitment to standardizing data across sources and building infrastructures that can produce fit-for-purpose RWD that will provide timely understanding of the effectiveness of individual interventions.

Guidelines for diagnosis and treatment of advanced breast cancer in China (2022 edition)

Breast cancer is the most common cancer among women worldwide. It has been estimated that about 416 000 new cases and over 117 000 deaths of breast cancer occurred in China in 2020. Among the new cases of breast cancer diagnosed each year, 3-10% have distant metastasis at the time of initial diagnosis. In addition, approximately 30% of patients with early-stage breast cancer may eventually experience recurrence or metastases. The 5-year survival rate of patients with advanced breast cancer is only 20% with a median overall survival of 2-3 years. Although advanced breast cancer remains incurable at present, new therapeutic options and multidisciplinary treatment could be utilized to alleviate symptoms, improve quality of life, and prolong patients' survival. The choice of treatment regimens for patients with advanced breast cancer is very important, and the optimal treatment strategy beyond the first- and second-line therapy is often lacking. Herein, the China Advanced Breast Cancer Guideline Panel discussed and summarized recent clinical evidence, updated the guidelines for the diagnosis and treatment of advanced breast cancer based on the 2020 edition, and formulated the "Guidelines for diagnosis and treatment of advanced breast cancer in China (2022 edition)" for clinicians' reference.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data

Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects' data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.

New Anticancer Drugs: Reliably Assessing "Value" While Addressing High Prices

Countries face challenges in paying for new drugs. High prices are driven in part by exploding drug development costs, which, in turn, are driven by essential but excessive regulation. Burdensome regulation also delays drug development, and this can translate into thousands of life-years lost. We need system-wide reform that will enable less expensive, faster drug development. The speed with which COVID-19 vaccines and AIDS therapies were developed indicates this is possible if governments prioritize it. Countries also differ in how they value drugs, and generally, those willing to pay more have better, faster access. Canada is used as an example to illustrate how "incremental cost-effectiveness ratios" (ICERs) based on measures such as gains in "quality-adjusted life-years" (QALYs) may be used to determine a drug's value but are often problematic, imprecise assessments. Generally, ICER/QALY estimates inadequately consider the impact of patient crossover or long post-progression survival, therapy benefits in distinct subpopulations, positive impacts of the therapy on other healthcare or societal costs, how much governments willingly might pay for other things, etc. Furthermore, a QALY value should be higher for a lethal or uncommon disease than for a common, nonlethal disease. Compared to international comparators, Canada is particularly ineffective in initiating public funding for essential new medications. Addressing these disparities demands urgent reform.

CDK Inhibitors and FDA: Approved and Orphan

The protein kinases are a large family of enzymes which catalyze protein phosphorylation at certain amino acids [...].

Tumor-agnostic baskets to N-of-1 platform trials and real-world data: Transforming precision oncology clinical trial design

Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the sine qua non of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, in silico modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.

Digital twins for health: a scoping review

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

Overall Survival With Palbociclib Plus Letrozole in Advanced Breast Cancer

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned coprimary or secondary analyses are not yet available. Clinical trial updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.PALOMA-2 demonstrated statistically and clinically significant improvement in progression-free survival with palbociclib plus letrozole versus placebo plus letrozole in estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+/HER2-) advanced breast cancer (ABC). Here, we report results for the secondary end point overall survival (OS). Postmenopausal women (N = 666) with ER+/HER2- ABC without previous systemic therapy for ABC were randomly assigned 2:1 to palbociclib plus letrozole or placebo plus letrozole. After a median follow-up of 90.1 months, 405 deaths were observed and 155 patients were known to be alive. The median OS was 53.9 months (95% CI, 49.8 to 60.8) with palbociclib plus letrozole versus 51.2 months (95% CI, 43.7 to 58.9) with placebo plus letrozole (hazard ratio [HR], 0.96 [95% CI, 0.78 to 1.18]; stratified one-sided P = .34). An imbalance in the number of patients with unknown survival outcome between the treatment arms (13.3% v 21.2%, respectively) limited interpretation of OS results. With recovered survival data, the median OS was 53.8 (95% CI, 49.8 to 59.2) versus 49.8 months (95% CI, 42.3 to 56.4), respectively (HR, 0.92 [95% CI, 0.76 to 1.12]; one-sided P = .21). OS was not significantly improved with palbociclib plus letrozole compared with placebo plus letrozole.

Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analyses generate hypotheses about patients' response to treatments.

A navitoclax-loaded nanodevice targeting matrix metalloproteinase-3 for the selective elimination of senescent cells

Cellular senescence is implicated in the occurrence and progression of multiple age-related disorders. In this context, the selective elimination of senescent cells, senolysis, has emerged as an effective therapeutic strategy. However, the heterogeneous senescent phenotype hinders the discovery of a universal and robust senescence biomarker that limits the effective of senolytic with off-target toxic effects. Therefore, the development of more selective strategies represents a promising approach to increase the specificity of senolytic therapy. In this study, we have developed an innovative nanodevice for the selective elimination of senescent cells (SCs) based on the specific enzymatic activity of the senescent secretome. The results revealed that when senescence is induced in proliferating WI-38 by ionizing radiation (IR), the cells secrete high levels of matrix metalloproteinase-3 (MMP-3). Based on this result, mesoporous silica nanoparticles (MSNs) were loaded with the senolytic navitoclax (Nav) and coated with a specific peptide which is substrate of MMP-3 (NPs(Nav)@MMP-3). Studies in cells confirmed the preferential release of cargo in IR-induced senescent cells compared to proliferating cells, depending on MMP-3 levels. Moreover, treatment with NPs(Nav)@MMP-3 induced a selective decrease in the viability of SCs as well as a protective effect on non-proliferating cells. These results demonstrate the potential use of NPs to develop enhanced senolytic therapies based on specific enzymatic activity in the senescent microenvironment, with potential clinical relevance. STATEMENT OF SIGNIFICANCE: The common β-galactosidase activity has been exploited to develop nanoparticles for the selective elimination of senescent cells. However, the identification of new senescent biomarkers is a key factor for the development of improved strategies. In this scenario, we report for the first time the development of NPs targeting senescent cells based on specific enzymatic activity of the senescent secretome. We report a navitoclax-loaded nanodevice responsive to the matrix metalloproteinase-3 (MMP-3) associated with the senescent phenotype. Our nanosystem achieves the selective release of navitoclax in an MMP-3-dependent manner while limiting off-target effects on non-senescent cells. This opens the possibility of using nanoparticles able to detect an altered senescent environment and selectively release its content, thus enhancing the efficacy of senolytic therapies.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Real-world treatment patterns for palbociclib plus an aromatase inhibitor, or an aromatase inhibitor alone, for patients with metastatic breast cancer in the Flatiron Database

There are limited real-world comparative effectiveness data for palbociclib plus an aromatase inhibitor (AI) as a first-line (1L) treatment examining endpoints that require long term follow-up and post 1L progression. The Flatiron Health Analytic Database was used to characterize treatment and dosing patterns in patients with hormone receptor-positive/human epidermal growth factor 2-negative (HR+/HER2-) metastatic breast cancer (mBC) receiving palbociclib plus an AI vs an AI alone in routine US clinical practice. In addition, time to chemotherapy (TTC) and real-world progression-free survival (rwPFS) when combining 1L and second-line of therapy (rwPFS2) were assessed. Of 1324 patients who received palbociclib plus an AI between February 3, 2015 and March 31, 2020, 1110 (83.8%) started palbociclib at the recommended 125 mg/day dose. After stabilized inverse probability treatment-weighting (sIPTW), median TTC in patients treated with palbociclib plus an AI and AI alone was 37.4 months (95% confidence interval [CI], 33.7-40.7) and 29.2 months (95% CI, 26.8-33.5), respectively (hazard ratio [HR] = 0.77 [95% CI, 0.69-0.86], P < .0001); median rwPFS2 was 32.6 months (95% CI, 29.4-35.2) and 20.7 months (95% CI, 18.9-22.6), respectively (HR = 0.62 [95% CI, 0.54-0.70], P < .0001). Sensitivity analyses with propensity score matching showed similar results to sIPTW analyses. Results from this large real-world study examining additional effectiveness outcomes beyond 1L rwPFS and overall survival support the use of palbociclib plus an AI as a 1L treatment for patients with HR+/HER2- mBC.

The CDK4/6 inhibitor revolution - a game-changing era for breast cancer treatment

Cyclin-dependent kinase (CDK) 4/6 inhibition in combination with endocrine therapy is the standard-of-care treatment for patients with advanced-stage hormone receptor-positive, HER2 non-amplified (HR+HER2-) breast cancer. These agents can also be administered as adjuvant therapy to patients with higher-risk early stage disease. Nonetheless, the clinical success of these agents has created several challenges, such as how to address acquired resistance, identifying which patients are most likely to benefit from therapy prior to treatment, and understanding the optimal timing of administration and sequencing of these agents. In this Review, we describe the rationale for targeting CDK4/6 in patients with breast cancer, including a summary of updated clinical evidence and how this should inform clinical practice. We also discuss ongoing research efforts that are attempting to address the various challenges created by the widespread implementation of these agents.

Outcomes of male patients with HR+/HER2- advanced breast cancer receiving palbociclib in the real-world POLARIS study

PURPOSE

Data on treatments for male breast cancer patients are limited owing to rarity and underrepresentation in clinical trials. The real-world POLARIS study gathers data on palbociclib use for the treatment of hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC) in female and male patients. This sub-analysis describes real-world palbociclib treatment patterns, clinical outcomes, and quality of life (QoL) in male patients.

METHODS

POLARIS is a prospective, noninterventional, multicenter, real-world study of patients with HR+/HER2- ABC receiving palbociclib. Assessments included medical record reviews, patient QoL questionnaires (European Organisation for Research and Treatment of Cancer Quality-of-Life Questionnaire-Core 30), site characteristics questionnaires, and physician treatment selection surveys. Variables included demographics, disease history, global health status/QoL, clinical assessments and adverse events. Analyses were descriptive in nature. For clinical outcomes, real-world tumor responses and progression were determined by physician assessment in routine clinical practice. Real-world progression-free survival (rwPFS) was described using the Kaplan-Meier method.

RESULTS

At data cutoff, 15 male patients were enrolled (median age, 66 years). Nine patients received palbociclib as a first-line treatment and 6 as a second-line or later treatment. Patients received a median of 20 cycles of palbociclib. Neutropenia was experienced by 2 patients and grade ≥ 3 adverse events were reported in 11 patients. Global health status/QoL scores remained generally consistent during the study. One patient (6.7%) achieved a complete tumor response, 4 (26.7%) a partial response, and 8 (53.3%) stable disease. Median rwPFS was 19.8 months (95% CI, 7.4-38.0). Median follow-up duration was 24.7 months (95% CI, 20.0-35.7).

CONCLUSION

This real-world analysis showed that palbociclib was well tolerated and provides preliminary data on treatment patterns and outcomes with palbociclib in male patients with HR+/HER2- ABC, helping inform the use of palbociclib in this patient subgroup.

TRIAL IDENTIFIER

NCT03280303.

Artificial intelligence across oncology specialties: current applications and emerging tools

Oncology is becoming increasingly personalised through advancements in precision in diagnostics and therapeutics, with more and more data available on both ends to create individualised plans. The depth and breadth of data are outpacing our natural ability to interpret it. Artificial intelligence (AI) provides a solution to ingest and digest this data deluge to improve detection, prediction and skill development. In this review, we provide multidisciplinary perspectives on oncology applications touched by AI-imaging, pathology, patient triage, radiotherapy, genomics-driven therapy and surgery-and integration with existing tools-natural language processing, digital twins and clinical informatics.

Enhancing immunogenic responses through CDK4/6 and HIF2α inhibition in Merkel cell carcinoma

Approximately 50% of Merkel cell carcinoma (MCC) patients facing this highly aggressive skin cancer initially respond positively to PD-1-based immunotherapy. Nevertheless, the recurrence of MCC post-immunotherapy emphasizes the pressing need for more effective treatments. Recent research has highlighted Cyclin-dependent kinases 4 and 6 (CDK4/6) as pivotal cell cycle regulators gaining prominence in cancer studies. This study reveals that the CDK4/6 inhibitor, palbociclib can enhance PD-L1 gene transcription and surface expression in MCC cells by activating HIF2α. Inhibiting HIF2α with TC-S7009 effectively counteracts palbociclib-induced PD-L1 transcription and significantly intensifies cell death in MCC. Simultaneously, co-targeting CDK4/6 and HIF2α boosts ROS levels while suppressing SLC7A11, a key regulator of cellular redox balance, promoting ferroptosis- a form of immunogenic cell death linked to iron. Considering the rising importance of immunogenic cell death in immunotherapy, this strategy holds promise for improving future MCC treatments, markedly increasing immunogenic cell death various across various MCC cell lines, thus advancing cancer immunotherapy.

Efficient Estimation under Data Fusion

We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including in the estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions, and rewards and one data source of the same covariates. In this work, we consider the general case where one or more data sources align with each part of the distribution of the target population, for example, the conditional distribution of the reward given actions and covariates. We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means to construct estimators that achieve these bounds. In numerical simulations, we illustrate marked improvements in efficiency from using our proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials.

Clinical Pharmacology Applications of Real-World Data and Real-World Evidence in Drug Development and Approval-An Industry Perspective

Since the 21st Century Cures Act was signed into law in 2016, real-world data (RWD) and real-world evidence (RWE) have attracted great interest from the healthcare ecosystem globally. The potential and capability of RWD/RWE to inform regulatory decisions and clinical drug development have been extensively reviewed and discussed in the literature. However, a comprehensive review of current applications of RWD/RWE in clinical pharmacology, particularly from an industry perspective, is needed to inspire new insights and identify potential future opportunities for clinical pharmacologists to utilize RWD/RWE to address key drug development questions. In this paper, we review the RWD/RWE applications relevant to clinical pharmacology based on recent publications from member companies in the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) RWD Working Group, and discuss the future direction of RWE utilization from a clinical pharmacology perspective. A comprehensive review of RWD/RWE use cases is provided and discussed in the following categories of application: drug-drug interaction assessments, dose recommendation for patients with organ impairment, pediatric plan development and study design, model-informed drug development (e.g., disease progression modeling), prognostic and predictive biomarkers/factors identification, regulatory decisions support (e.g., label expansion), and synthetic/external control generation for rare diseases. Additionally, we describe and discuss common sources of RWD to help guide appropriate data selection to address questions pertaining to clinical pharmacology in drug development and regulatory decision making.

Outcomes for Dostarlimab and Real-World Treatments in Post-platinum Patients With Advanced/Recurrent Endometrial Cancer: The GARNET Trial Versus a US Electronic Health Record-Based External Control Arm

Background: Patients with advanced or recurrent endometrial cancer (EC) have limited treatment options following platinum-based chemotherapy and poor prognosis. The single-arm, Phase I GARNET trial (NCT02715284) previously reported dostarlimab efficacy in mismatch repair-deficient/microsatellite instability-high advanced or recurrent EC. Objectives: The objective of this study was to compare overall survival (OS) and describe time to treatment discontinuation (TTD) for dostarlimab (GARNET Cohort A1 safety population) with an equivalent real-world external control arm receiving non-anti-programmed death (PD)-1/PD-ligand (L)1/2 treatments (constructed using data from a nationwide electronic health record-derived de-identified database and applied GARNET eligibility criteria). Methods: Propensity scores constructed from prognostic factors, identified by literature review and clinical experts, were used for inverse probability of treatment weighting (IPTW). Kaplan-Meier curves were constructed and OS/TTD was estimated (Cox regression model was used to estimate the OS-adjusted hazard ratio). Results: Dostarlimab was associated with a 52% lower risk of death vs real-world treatments (hazard ratio, 0.48; 95% confidence interval [CI], 0.35-0.66). IPTW-adjusted median OS for dostarlimab (N=143) was not estimable (95% CI, 19.4-not estimable) versus 13.1 months (95% CI, 8.3-15.9) for real-world treatments (N = 185). Median TTD was 11.7 months (95% CI, 6.0-38.7) for dostarlimab and 5.3 months (95% CI, 4.1-6.0) for the real-world cohort. Discussion: Consistent with previous analyses, patients treated with dostarlimab had significantly longer OS than patients in the US real-world cohort after adjusting for the lack of randomization using stabilized IPTW. Additionally, patients had a long TTD when treated with dostarlimab, suggesting a favorable tolerability profile. Conclusion: Patients with advanced or recurrent EC receiving dostarlimab in GARNET had significantly lower risk of death than those receiving real-world non-anti-PD-(L)1/2 treatments.

A real-world study of the first use of palbociclib for the treatment of advanced breast cancer within the UK National Health Service as part of the novel Ibrance® Patient Program

BACKGROUND

The Ibrance® Patient Program was established to provide access to palbociclib for UK National Health Service (NHS) patients with metastatic breast cancer (MBC), pending a funding decision.

METHODS

Non-interventional cohort study involving a retrospective medical record review of patients commenced on palbociclib between April and December 2017 at eight UK centres. Primary outcomes included clinicopathological characteristics, treatment patterns, clinical outcomes and selected adverse events.

RESULTS

Overall, 191 patients were identified, median age of 57.0 years (range 24.3-90.9); 30% were diagnosed with de novo MBC; 72% received first-line and 10% as ≥ second-line treatment. Median progression-free survival (95% CI) was 22.8 months (16.5-not reached [NR]) in first-line; NR in patients with de novo MBC; 7.8 months (6.8-NR) in ≥ second-line (median follow-up: 24 months). Median overall survival (OS) was NR in the overall cohort; OS rate (95% CI) at 24 months was 74.2% (67.1-81.9%) in first-line; 82.1% (72.6-92.8%) in patients with de novo MBC; 55.0% (37.0-81.8%) in ≥ second-line. Forty-seven per cent of patients developed grade 3-4 neutropenia; 3% febrile neutropenia.

CONCLUSION

This study supports the effectiveness of palbociclib and demonstrates the benefit to patients of early access schemes that bridge the gap between regulatory approval and NHS funding for new medicines.

CLINICAL TRIAL REGISTRATION

Clinical trial: ClinicalTrial.gov:NCT03921866.

Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer

Breast cancer is the second leading cause of death for women worldwide. The heterogeneity of this disease presents a big challenge in its therapeutic management. However, recent advances in molecular biology and immunology enable to develop highly targeted therapies for many forms of breast cancer. The primary objective of targeted therapy is to inhibit a specific target/molecule that supports tumor progression. Ak strain transforming, cyclin-dependent kinases, poly (ADP-ribose) polymerase, and different growth factors have emerged as potential therapeutic targets for specific breast cancer subtypes. Many targeted drugs are currently undergoing clinical trials, and some have already received the FDA approval as monotherapy or in combination with other drugs for the treatment of different forms of breast cancer. However, the targeted drugs have yet to achieve therapeutic promise against triple-negative breast cancer (TNBC). In this aspect, immune therapy has come up as a promising therapeutic approach specifically for TNBC patients. Different immunotherapeutic modalities including immune-checkpoint blockade, vaccination, and adoptive cell transfer have been extensively studied in the clinical setting of breast cancer, especially in TNBC patients. The FDA has already approved some immune-checkpoint blockers in combination with chemotherapeutic drugs to treat TNBC and several trials are ongoing. This review provides an overview of clinical developments and recent advancements in targeted therapies and immunotherapies for breast cancer treatment. The successes, challenges, and prospects were critically discussed to portray their profound prospects.

Synthesis and biological evaluation of novel pteridin-7(8H)-one derivatives as potent CDK2 inhibitors

Cyclin-dependent kinase 2 (CDK2) is considered as an important target in the research of antitumor drugs. Taking the CDK2/4/6 inhibitor Ebvaciclib as the positive control and an in-house library compound (23) as the lead compound, three classes of 30 target compounds with pteridin-7(8H)-one as the core structure were designed to establish structure-activity relationships (SAR). In general, SAR of pteridin-7(8H)-one CDK2 inhibitors is systematically described in this paper, resulting in the discovery of two compounds (KII-17 and KII-21) with further research value. After the above compounds were tested for CDK2/4/6 kinase selectivity, we found that compound KII-21 was about 3 and 4 times more selective to CDK2-cyclinE2 than CDK4-cyclinD1 and CDK6-cyclinD3, respectively. This work also provides a reference basis for the subsequent research on CDK2 inhibitors.

Onconephrology and Thrombotic Microangiopathy: Looking Beyond the Horizon

Thrombotic microangiopathies (TMAs) represent a complex interaction of endothelial and podocyte biology, nephron physiology, complement genetics, and oncologic therapies with host immunology. The complexity of various factors, such as molecular causes, genetic expressions, and immune system mimicking, along with incomplete penetrance, make it difficult to find a straightforward solution. As a result, there may be variations in diagnosis, study, and treatment approaches, and achieving a consensus can be challenging. Here, we review the molecular biology, pharmacology, immunology, molecular genetics, and pathology of the various TMA syndromes in the setting of cancer. Controversies in etiology, nomenclature, and points requiring further clinical, translational, and bench research are discussed. Complement-mediated TMAs, chemotherapy drug-mediated TMAs, TMAs in monoclonal gammopathy, and other TMAs central to onconephrology practice are reviewed in detail. In addition, established and emerging therapies within the US Food and Drug Administration pipeline subsequently are discussed. Finally, a comprehensive review of critical areas of onconephrology clinical practice is presented as practical value to the clinical practitioner and seeds of investigation to be sown among the community of atypical hemolytic uremic syndrome researchers.

Characteristics and Outcomes in Cases of US Male Patients with Metastatic Breast Cancer Receiving Abemaciclib in Routine Clinical Practice

INTRODUCTION

Breast cancer in males constitutes approximately 1% of all breast cancer cases globally. Despite extensive treatment experience with abemaciclib in women with metastatic breast cancer (MBC), real-world evidence in male MBC is lacking.

METHODS

This analysis was a part of a broader, retrospective study that analyzed electronic medical records and charts of 448 men and women with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) MBC who initiated an abemaciclib-containing regimen from January 2017 through September 2019. Data were collected from the Florida Cancer Specialists & Research Institute and the Electronic Medical Office Logistics Health Oncology Warehouse Language™ databases and summarized descriptively. Real-world best response was described: complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD).

RESULTS

Data for six male patients with MBC who were treated with abemaciclib in combination with an aromatase inhibitor (AI) or fulvestrant are presented. Four patients were aged ≥ 75 years, and four patients had ≥ 3 metastatic sites, including visceral involvement. Abemaciclib was initiated in/after third-line (≥ 3L) in four patients, and patients had history of treatment with AI (n = 4), chemotherapy (n = 3), and/or prior cyclin-dependent kinase 4 and 6 inhibitors (n = 2) in the metastatic setting. Abemaciclib + fulvestrant was the most common abemaciclib-containing regimen (n = 4). Best response was documented in four patients: 1 each with CR, PR, SD, and PD.

CONCLUSION

Prevalence of male MBC in this dataset was consistent with expected prevalence in the broader population. Most male patients received an abemaciclib-containing regimen in ≥ 3L, with anti-cancer activity observed despite heavy metastatic burden and prior treatments in a metastatic setting.

Recent Advances in Covalent Drug Discovery

In spite of the increasing number of biologics license applications, the development of covalent inhibitors is still a growing field within drug discovery. The successful approval of some covalent protein kinase inhibitors, such as ibrutinib (BTK covalent inhibitor) and dacomitinib (EGFR covalent inhibitor), and the very recent discovery of covalent inhibitors for viral proteases, such as boceprevir, narlaprevir, and nirmatrelvir, represent a new milestone in covalent drug development. Generally, the formation of covalent bonds that target proteins can offer drugs diverse advantages in terms of target selectivity, drug resistance, and administration concentration. The most important factor for covalent inhibitors is the electrophile (warhead), which dictates selectivity, reactivity, and the type of protein binding (i.e., reversible or irreversible) and can be modified/optimized through rational designs. Furthermore, covalent inhibitors are becoming more and more common in proteolysis, targeting chimeras (PROTACs) for degrading proteins, including those that are currently considered to be 'undruggable'. The aim of this review is to highlight the current state of covalent inhibitor development, including a short historical overview and some examples of applications of PROTAC technologies and treatment of the SARS-CoV-2 virus.

Creation of an ustekinumab external control arm for Crohn's disease using electronic health records data: A pilot study

BACKGROUND

Randomized trials are the gold-standard for clinical evidence generation, but they can sometimes be limited by infeasibility and unclear generalizability to real-world practice. External control arm (ECA) studies may help address this evidence gaps by constructing retrospective cohorts that closely emulate prospective ones. Experience in constructing these outside the context of rare diseases or cancer is limited. We piloted an approach for developing an ECA in Crohn's disease using electronic health records (EHR) data.

METHODS

We queried EHR databases and manually screened records at the University of California, San Francisco to identify patients meeting the eligibility criteria of TRIDENT, a recently completed interventional trial involving an ustekinumab reference arm. We defined timepoints to balance missing data and bias. We compared imputation models by their impacts on cohort membership and outcomes. We assessed the accuracy of algorithmic data curation against manual review. Lastly, we assessed disease activity following treatment with ustekinumab.

RESULTS

Screening identified 183 patients. 30% of the cohort had missing baseline data. Nonetheless, cohort membership and outcomes were robust to the method of imputation. Algorithms for ascertaining non-symptom-based elements of disease activity using structured data were accurate against manual review. The cohort consisted of 56 patients, exceeding planned enrollment in TRIDENT. 34% of the cohort was in steroid-free remission at week 24.

CONCLUSION

We piloted an approach for creating an ECA in Crohn's disease from EHR data by using a combination of informatics and manual methods. However, our study reveals significant missing data when standard-of-care clinical data are repurposed. More work will be needed to improve the alignment of trial design with typical patterns of clinical practice, and thereby enable a future of more robust ECAs in chronic diseases like Crohn's disease.

CDK4/6 inhibitor palbociclib promotes SARS-CoV-2 cell entry by down-regulating SKP2 dependent ACE2 degradation

Coronavirus disease 2019 (COVID-19) outbreak has become a global pandemic. CDK4/6 inhibitor palbociclib was reported to be one of the top-scored repurposed drugs to treat COVID-19. As the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry, expression level of angiotensin-converting enzyme 2 (ACE2) is closely related to SARS-CoV-2 infection. In this study, we demonstrated that palbociclib and other methods could arrest cells in G0/G1 phase and up-regulate ACE2 mRNA and protein levels without altering its subcellular localization. Palbociclib inhibited ubiquitin-proteasome and lysosomal degradation of ACE2 through down-regulating S-phase kinase-associated protein 2 (SKP2). In addition, increased ACE2 expression induced by palbociclib and other cell cycle arresting compounds facilitated pseudotyped SARS-CoV-2 infection. This study suggested that ACE2 expression was down-regulated in proliferating cells. Cell cycle arresting compounds could increase ACE2 expression and facilitate SARS-CoV-2 cell entry, which may not be suitable therapeutic agents for the treatment of SARS-CoV-2 infection.

Defining genomic, transcriptomic, proteomic, epigenetic, and phenotypic biomarkers with prognostic capability in male breast cancer: a systematic review

Although similar phenotypically, there is evidence that male and female breast cancer differ in their molecular landscapes. In this systematic review, we consolidated all existing prognostic biomarker data in male breast cancer spanning genetics, transcriptomics, proteomics, and epigenetics, and phenotypic features of prognostic value from articles published over a 29-year period (March 16, 1992, to May 1, 2021). We identified knowledge gaps in the existing literature, discussed limitations of the included studies, and outlined potential approaches for translational biomarker discovery and validation in male breast cancer. We also recognised STC2, DDX3, and DACH1 as underexploited markers of male-specific prognostic value in breast cancer. Finally, beyond describing the cumulative knowledge on the extensively researched markers oestrogen receptor-α, progesterone receptor, HER2, androgen receptor, and BRCA2, we highlighted ATM, CCND1, FGFR2, GATA3, HIF1-α, MDM2, TP53, and c-Myc as well studied predictors of poor survival that also aligned with several hallmarks of cancer.

Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration

PURPOSE

The SingHealth-Duke-GlaxoSmithKline COPD and Asthma Real-world Evidence (SDG-CARE) collaboration was formed to accelerate the use of Singaporean real-world evidence in research and clinical care. A centerpiece of the collaboration was to develop a near real-time database from clinical and operational data sources to inform healthcare decision making and research studies on asthma and chronic obstructive pulmonary disease (COPD).

METHODS

Our multidisciplinary team, including clinicians, epidemiologists, data scientists, medical informaticians and IT engineers, adopted the hybrid waterfall-agile project management methodology to develop the SingHealth COPD and Asthma Data Mart (SCDM). The SCDM was developed within the organizational data warehouse. It pulls and maps data from various information systems using extract, transform and load (ETL) pipelines. Robust user testing and data verification was also performed to ensure that the business requirements were met and that the ETL pipelines were valid.

RESULTS

The SCDM includes 199 data elements relevant to asthma and COPD. Data verification was performed and found the SCDM to be reliable. As of December 31, 2019, the SCDM contained 36,407 unique patients with asthma and COPD across the spectrum from primary to tertiary care in our healthcare system. The database updates weekly to add new data of existing patients and to include new patients who fulfil the inclusion criteria.

CONCLUSIONS

The SCDM was systematically developed and tested to support the use RWD for clinical and health services research in asthma and COPD. This can serve as a platform to provide research and operational insights to improve the care delivered to our patients.

Inhibition of p21 activates Akt kinase to trigger ROS-induced autophagy and impacts on tumor growth rate

Owing to its ability to induce cellular senescence, inhibit PCNA, and arrest cell division cycle by negatively regulating CDKs as well as being a primary target of p53, p21 is traditionally considered a tumor suppressor. Nonetheless, several reports in recent years demonstrated its pro-oncogenic activities such as apoptosis inhibition by cytosolic p21, stimulation of cell motility, and promoting assembly of cyclin D-CDK4/6 complex. These opposing effects of p21 on cell proliferation, supported by the observations of its inconsistent expression in human cancers, led to the emergence of the concept of "antagonistic duality" of p21 in cancer progression. Here we demonstrate that p21 negatively regulates basal autophagy at physiological concentration. Akt activation, upon p21 attenuation, driven ROS accumulation appears to be the major underlying mechanism in p21-mediated modulation of autophagy. We also find p21, as a physiological inhibitor of autophagy, to have oncogenic activity during early events of tumor development while its inhibition favors survival and growth of cancer cells in the established tumor. Our data, thereby, reveal the potential role of autophagy in antagonistic functional duality of p21 in cancer.

For insights into the real world, consider real-world data

Randomized control trials (RCTs) are required before drug and device approvals and have contributed to patient safety, but they have also increased the cost and time of regulatory assessments. We propose that using real-world evidence to complement or, in some settings, to replace RCTs will accelerate delivery of new drugs to patients.

PERFORM: a non-interventional study assessing the patients' treatment starting with 1L palbociclib in HR+/HER2- ABC

The prospective, non-interventional PERFORM study describes and analyzes the effectiveness of palbociclib in combination with endocrine therapy (aromatase inhibitor or fulvestrant) as first-line treatment for patients with locally advanced or metastatic HR+/HER2- breast cancer in the real-world setting in Germany and Austria. PERFORM will reflect current patient characteristics and routine treatment patterns including treatment sequences and time to subsequent (chemo)therapy. Besides, second-line treatment effectiveness and patient-relevant end points such as longitudinal patient-reported outcome measurements beyond disease progression will be analyzed. Accounting for the heterogenous real-world patient population, data on clinicopathologic subgroups underrepresented in clinical trials such as elderly or male will be analyzed. Taken together, PERFORM will close knowledge gaps from clinical trials in real world.

Intravenous edaravone treatment in ALS and survival: An exploratory, retrospective, administrative claims analysis

BACKGROUND

We aimed to evaluate overall survival in US patients with amyotrophic lateral sclerosis (ALS) treated with intravenous (IV) edaravone compared with those not treated with IV edaravone in a real-world setting.

METHODS

This exploratory retrospective comparative effectiveness observational analysis included patients with ALS who were enrolled in an administrative claims database from 8 August 2017 to 31 March 2020. Propensity score matching identified IV edaravone-treated patients (cases) and non-edaravone-treated patients (controls) matched for covariates: age, race, geographic region, sex, pre-index disease duration, insurance, history of cardiovascular disease, riluzole prescription, gastrostomy tube placement, artificial nutrition, noninvasive ventilation, and all-cause hospitalisation. For cases, the index date was the date of the first claim for IV edaravone. For controls, it was the date IV edaravone was available (8 August 2017). The effect of IV edaravone on all-cause mortality was estimated with shared frailty Cox regression analysis.

FINDINGS

318 cases were matched to 318 controls. In both groups, 208 patients (65.4%) had a history of riluzole prescription. As of 31 March 2021, there were 155 deaths (48.7%) among the cases and 196 among the controls (61.6%). Median overall survival time was 29.5 months with edaravone and 23.5 months without, respectively, and the risk of death was 27% lower in cases than in controls (HR, 0.73; 95% CI, 0.59-0.91; p=0.005).

INTERPRETATION

In this real-world analysis, IV edaravone treatment in a large predominantly riluzole-treated US cohort was associated with prolonged overall survival compared with not using IV edaravone. Data from adequately powered RCTs are needed to support this finding.

FUNDING

Funded by Mitsubishi Tanabe Pharma America.

Clinical trial design in the era of precision medicine

Recent rapid biotechnological breakthroughs have led to the identification of complex and unique molecular features that drive malignancies. Precision medicine has exploited next-generation sequencing and matched targeted therapy/immunotherapy deployment to successfully transform the outlook for several fatal cancers. Tumor and liquid biopsy genomic profiling and transcriptomic, immunomic, and proteomic interrogation can now all be leveraged to optimize therapy. Multiple new trial designs, including basket and umbrella trials, master platform trials, and N-of-1 patient-centric studies, are beginning to supplant standard phase I, II, and III protocols, allowing for accelerated drug evaluation and approval and molecular-based individualized treatment. Furthermore, real-world data, as well as exploitation of digital apps and structured observational registries, and the utilization of machine learning and/or artificial intelligence, may further accelerate knowledge acquisition. Overall, clinical trials have evolved, shifting from tumor type-centered to gene-directed and histology-agnostic trials, with innovative adaptive designs and personalized combination treatment strategies tailored to individual biomarker profiles. Some, but not all, novel trials now demonstrate that matched therapy correlates with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To further improve the precision medicine paradigm, the strategy of matching drugs to patients based on molecular features should be implemented earlier in the disease course, and cancers should have comprehensive multi-omic (genomics, transcriptomics, proteomics, immunomic) tumor profiling. To overcome cancer complexity, moving from drug-centric to patient-centric individualized combination therapy is critical. This review focuses on the design, advantages, limitations, and challenges of a spectrum of clinical trial designs in the era of precision oncology.

Molecular perspective on targeted therapy in breast cancer: a review of current status

Breast cancer is categorized at the molecular level according to the status of certain hormone and growth factor receptors, and this classification forms the basis of current diagnosis and treatment. The development of resistance to treatment and recurrence of the disease have led researchers to develop new therapies. In recent years, most of the research in the field of oncology has focused on the development of targeted therapies, which are treatment methods developed directly against molecular abnormalities. Promising advances have been made in clinical trials investigating the effect of these new treatment modalities and their combinations with existing therapeutic treatments in the treatment of breast cancer. Monoclonal antibodies, tyrosine kinase inhibitors, antibody–drug conjugates, PI3K/Akt/mTOR pathway inhibitors, cyclin-dependent kinase 4/6 inhibitors, anti-angiogenic drugs, PARP inhibitors are among the targeted therapies used in breast cancer treatment. In this review, we aim to present a molecular view of recently approved target agents used in breast cancer.

An Approach to Solving the Complex Clinicogenomic Data Landscape in Precision Oncology: Learnings From the Design of WAYFIND-R, a Global Precision Oncology Registry

Precision oncology, where patients are given therapies based on their genomic profile and disease trajectory, is rapidly evolving to become a pivotal part of cancer management, supported by regulatory approvals of biomarker-matched targeted therapies and cancer immunotherapies. However, next-generation sequencing (NGS)-based technologies have revealed an increasing number of molecular-based cancer subtypes with rare patient populations, leading to difficulties in executing/recruiting for traditional clinical trials. Therefore, approval of novel therapeutics based on traditional interventional studies may be difficult and time consuming, with delayed access to innovative therapies. Real-world data (RWD) that describe the patient journey in routine clinical practice can help elucidate the clinical utility of NGS-based genomic profiling, multidisciplinary case discussions, and targeted therapies. We describe key learnings from the setup of WAYFIND-R (NCT04529122), a first-of-its-kind global cancer registry collecting RWD from patients with solid tumors who have undergone NGS-based genomic profiling. The meaning of 'generalizability' and 'high quality' for RWD across different geographic areas was revisited, together with patient recruitment processes, and data sharing and privacy. Inspired by these learnings, WAYFIND-R's design will help physicians discuss patient treatment plans with their colleagues, improve understanding of the impact of treatment decisions/cancer care processes on patient outcomes, and provide a platform to support the design and conduct of further clinical/epidemiologic research. WAYFIND-R demonstrates user-friendly, electronic case report forms, standardized collection of molecular tumor board-based decisions, and a dashboard providing investigators with access to local cohort-level data and the ability to interact with colleagues or search the entire registry to find rare populations. Overall, WAYFIND-R will inform on best practice for NGS-based treatment decisions by clinicians, foster global collaborations between cancer centers and enable robust conclusions regarding outcome data to be drawn, improve understanding of disparities in patients' access to advanced diagnostics and therapies, and ultimately drive advances in precision oncology.

Replication of Overall Survival, Progression-Free Survival, and Overall Response in Chemotherapy Arms of Non-Small Cell Lung Cancer Trials Using Real-World Data

PURPOSE

The utility of real-world data (RWD) for use as external controls in drug development is informed by studies that replicate trial control arms for different endpoints. The purpose of this study was to replicate control arms from four non-small cell lung cancer (NSCLC) randomized controlled trials (RCT) to analyze overall survival (OS), progression-free survival (PFS), and overall response rate (ORR) using RWD.

PATIENTS AND METHODS

This study used RWD from a nationwide de-identified database and a clinico-genomic database to replicate OS, PFS, and ORR endpoints in the chemotherapy control arms of four first-line NSCLC RCTs evaluating atezolizumab [IMpower150-wild-type (WT), IMpower130-WT, IMpower131, and IMpower132]. Additional objectives were to develop a definition of real-world PFS (rwPFS) and to evaluate the real-world response rate (rwRR) endpoint.

RESULTS

Baseline demographic and clinical characteristics were balanced after application of propensity score weighting methods. For rwPFS and OS, RWD external controls were generally similar to their RCT control counterparts. Across all four trials, the hazard ratio (HR) point estimates comparing trial controls with external controls were closer to 1.0 for the PFS endpoint than for the OS endpoint. An exploratory assessment of rwRR in RWD revealed a slight but nonsignificant overestimation of RCT ORR, which was unconfounded by baseline characteristics.

CONCLUSIONS

RWD can be used to reasonably replicate the OS and PFS of chemotherapy control arms of first-line NSCLC RCTs. Additional studies can provide greater insight into the utility of RWD in drug development.