Recent advancements in large language models (LLMs) enable real-time web search, improved referencing, and multilingual support, yet ensuring they provide safe health information remains crucial. This perspective evaluates seven publicly accessible LLMs—ChatGPT, Co-Pilot, Gemini, MetaAI, Claude, Grok, Perplexity—on three simple cancer-related queries across eight languages (336 responses: English, French, Chinese, Thai, Hindi, Nepali, Vietnamese, and Arabic). None of the 42 English responses contained clinically meaningful hallucinations, whereas 7 of 294 non-English responses did. 48% (162/336) of responses included valid references, but 39% of the English references were.com links reflecting quality concerns. English responses frequently exceeded an eighth-grade level, and many non-English outputs were also complex. These findings reflect substantial progress over the past 2-years but reveal persistent gaps in multilingual accuracy, reliable reference inclusion, referral practices, and readability. Ongoing benchmarking is essential to ensure LLMs safely support global health information dichotomy and meet online information standards.

11042 Background: Sex-based differences in outcomes with contemporary oncology treatments remain underexplored, creating a gap in the evidence required for personalised care. To address this, our objective was to systematically evaluate whether sex differences exist in survival and adverse event outcomes with modern anticancer therapies. Methods: Individual patient data (IPD) was accessed via the Vivli platform from 60 clinical trials supporting US Food and Drug Administration approvals of anticancer medicines for the treatment of solid tumours from 2012 to 2022. Of these, 39 trials were included in analysing sex-based differences in clinical outcomes (i.e. breast, prostate, and ovarian cancer were excluded) Two-stage IPD meta-analysis approaches were employed. First, Cox proportional hazards models were applied to estimate hazard ratios (HRs) with confidence intervals for overall survival (OS), progression-free survival (PFS), and grade ≥3 adverse events (AEs) outcomes by sex within each clinical trial. Complete case analyses were conducted, with adjustments for covariates including age, race, ECOG performance status, and weight, as well as study-level factors, such as randomisation arm stratification. The results for each clinical trial were then pooled using random-effects meta-analysis. Subgroup analyses were performed to evaluate findings by cancer and treatment types. Results: Data from 39 trials for solid tumours (n=20,806; females=8,367) were analysed, including non-small cell lung (n=19), melanoma (n=6), colorectal (n=3), urothelial (n=2), gastric (n=2), and other (n=7) cancers. Treatment regimens evaluated included immunotherapies (n=9 trials), chemotherapies (n=18), and targeted therapies (n=32). In adjusted analyses, females demonstrated favourable OS (HR 0.78, 95% CI: 0.72–0.84; I² = 60%, p <0.001) and PFS (HR 0.84, 95% CI: 0.80–0.89; I² = 47%, p <0.001) compared to males. However, females had a higher risk of grade ≥3 AEs (HR 1.12, 95% CI: 1.05–1.18; I² = 40%, p <0.001). Subgroup analyses by cancer type and treatment regimen showed consistent trends in survival and AE outcomes according to sex. Conclusions: This meta-analysis, highlights consistency in females experiencing improved survival but higher toxicity compared to males with contemporary oncology treatments. These findings underscore the need to incorporate and prioritise sex as a key biological variable in trial design, dose optimisation, outcome analysis, and clinical decision-making within the oncology setting. Acknowledgement This publication is based on research using data from AstraZeneca, Boehringer Ingelheim, Daiichi Sankyo, Eli Lilly and Company, Hoffmann-La Roche, Janssen, Pfizer, Sanofi, and Takeda that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.

Tumour mutational burden (TMB) is an established biomarker for patients treated with immune checkpoint inhibitors (ICIs). The optimal TMB cut-off is uncertain. It is also uncertain whether there is a sharp TMB threshold or a more graduated change in clinical outcomes as TMB increases. We aimed to determine the relationship between TMB and ICI treatment outcomes using alternative statistical approaches in patients with non-small cell lung cancer. Tumour mutational burden was evaluated as a prognostic and predictive biomarker in advanced non-small cell lung cancer utilising data from two real-world cohorts of ICI use (n = 968) and three randomised controlled trials evaluating ICIs (n = 1588). The non-linear relationship between continuous TMB and response/survival/efficacy outcomes was evaluated using statistical methods that do not require specifying a TMB cut-off. Median TMB for all cohorts was seven mutations/megabase, excluding MYSTIC, where the median was 13 mutations/megabase. Progressively higher TMB was significantly associated with a progressively higher objective response rate and progression-free survival in ICI-treated patients in Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets [MSK-IMPACT] (objective response rate: p < 0.001, progression-free survival: p < 0.001), Strata Clinical Molecular Database [SCMD] (progression-free survival: p = 0.023) and OAK/POPLAR (objective response rate: p = 0.017, progression-free survival: p < 0.001) This relationship was not apparent for patients treated with chemotherapy. There was no obvious TMB threshold for ICI response. The relationship between TMB and overall survival was more complex and heterogeneous. Using a single cut-off to analyse a continuous biomarker may hide important information. Methods that provide more nuance to the underlying relationship between TMB and outcomes enable readers to judge for themselves the value and limitations of TMB cut-offs proposed for clinical practice.

Extracellular vesicles (EVs) are nanosized, membrane-bound particles released by virtually all cell types, serving as messengers within tissues and across organs via the bloodstream. EVs encapsulate diverse molecular cargo that reflects the phenotypic state of their originating cells, making them promising candidates for liquid biopsy applications. However, the heterogeneity of circulating EVs, comprising particles from various cell types and non-vesicular entities like lipoproteins, poses significant challenges for isolating tissue-specific EV populations. This review examines current methodologies for detecting and isolating tissue-specific EVs from blood, focusing on immunoaffinity capture (IAC) strategies that leverage surface marker expression for specificity. Key considerations, including the selection and validation of markers, are discussed alongside advances in EV subtyping and isolation protocols. Challenges such as marker cross-reactivity, EV biogenesis and transport dynamics are highlighted to underscore the complexity of achieving clinical utility. By providing an overview of validated tissue-specific markers and isolation techniques, this review aims to facilitate the development of EV-based biomarkers with enhanced specificity and sensitivity, enabling minimally invasive monitoring of organ function and disease.

The use of Immune checkpoint inhibitors (ICIs) as monotherapy for patients with hepatocellular carcinoma (HCC) has been associated with an increased risk of hyperprogressive disease (HPD), the occurrence of which carries a poor prognosis. However, it is unknown whether contemporary frontline treatment with the combination of atezolizumab and bevacizumab causes significant HPD. This study conducted a secondary analysis of patient-level data from the IMbrave150 randomized controlled trial of atezolizumab plus bevacizumab versus sorafenib for frontline treatment of HCC. Multiple established definitions of early progression and treatment failure applicable to clinical trials were evaluated, including Response Evaluation Criteria in Solid Tumours (RECIST) HPD, HPD based on percent change of sum of longest diameter (SLD HPD), treatment failure HPD (TF HPD), and fast progression (FP). The incidence of these measures was compared between arms. The risk factors for and prognosis of TF HPD were evaluated. The risk of RECIST HPD and TF HPD was significantly lower with atezolizumab plus bevacizumab treatment than with sorafenib treatment-odds ratio for RECIST HPD: 0.29 (95% CI 0.01 to 0.82), TF HPD: 0.30 (0.17, 0.54). TF HPD was similarly associated with poor prognosis, irrespective of treatment arm. High blood alpha-fetoprotein and neutrophil-to-lymphocyte ratio were both associated with an increased risk of TF HPD. For all definitions of early progression/treatment failure, the risk was either significantly lower with atezolizumab plus bevacizumab than with sorafenib, or there were no differences. Atezolizumab plus bevacizumab treatment is unlikely to cause significant HPD.

Large language models (LLMs) offer substantial promise for improving health care; however, some risks warrant evaluation and discussion. This study assessed the effectiveness of safeguards in foundational LLMs against malicious instruction into health disinformation chatbots. Five foundational LLMs-OpenAI's GPT-4o, Google's Gemini 1.5 Pro, Anthropic's Claude 3.5 Sonnet, Meta's Llama 3.2-90B Vision, and xAI's Grok Beta-were evaluated via their application programming interfaces (APIs). Each API received system-level instructions to produce incorrect responses to health queries, delivered in a formal, authoritative, convincing, and scientific tone. Ten health questions were posed to each customized chatbot in duplicate. Exploratory analyses assessed the feasibility of creating a customized generative pretrained transformer (GPT) within the OpenAI GPT Store and searched to identify if any publicly accessible GPTs in the store seemed to respond with disinformation. Of the 100 health queries posed across the 5 customized LLM API chatbots, 88 (88%) responses were health disinformation. Four of the 5 chatbots (GPT-4o, Gemini 1.5 Pro, Llama 3.2-90B Vision, and Grok Beta) generated disinformation in 100% (20 of 20) of their responses, whereas Claude 3.5 Sonnet responded with disinformation in 40% (8 of 20). The disinformation included claimed vaccine-autism links, HIV being airborne, cancer-curing diets, sunscreen risks, genetically modified organism conspiracies, attention deficit-hyperactivity disorder and depression myths, garlic replacing antibiotics, and 5G causing infertility. Exploratory analyses further showed that the OpenAI GPT Store could currently be instructed to generate similar disinformation. Overall, LLM APIs and the OpenAI GPT Store were shown to be vulnerable to malicious system-level instructions to covertly create health disinformation chatbots. These findings highlight the urgent need for robust output screening safeguards to ensure public health safety in an era of rapidly evolving technologies.

Researchers at the EORTC recently recommended clinical thresholds for the QLQ‐C30 to facilitate actionable insights in clinical practice. We evaluate the distribution of these thresholds and associations with outcomes in breast cancer. Data were pooled from two early‐stage and six advanced‐stage breast cancer trials. EORTC thresholds were applied to available QLQ‐C30 data to identify clinically important PRO domains. Associations between the number of clinically important PRO domains at baseline with overall survival (OS), invasive‐disease‐free survival (IDFS), progression‐free survival (PFS), grade ≥3 adverse events (AEs), and serious AEs were evaluated using Cox‐regression. Data from 8544 breast cancer patients, of whom 2428 (41%) of the 5893 early‐stage and 1486 (56%) of the 2651 advanced‐stage patients reported ≥3 clinically important PRO domains. In the early‐stage, each additional clinically important PRO domain was associated with worsened grade ≥3 AEs (HR, 1.03 [95%CI, 1.01–1.04], p = 0.001) and serious AEs (1.05 [1.03–1.07], p < 0.001). In the advanced‐stage, each additional clinically important PRO domain was associated with worsened OS (1.05 [1.03–1.07], p < 0.001), PFS (1.03 [1.01–1.04], p = 0.002), grade ≥3 AEs (1.04 [1.02–1.06], p < 0.001), and serious AEs (1.07 [1.04–1.11], p < 0.001). A substantial proportion of breast cancer patients report clinically important PRO domains at baseline, with increasing numbers associated with worsening AEs, survival, and quality‐of‐life.

OBJECTIVES:Clinical study reports (CSRs) are highly detailed documents that play a pivotal role in medicine approval processes. Though not historically publicly available, in recent years, major entities including the European Medicines Agency (EMA), Health Canada, and the US Food and Drug Administration (FDA) have highlighted the importance of CSR accessibility. The primary objective herein was to determine the proportion of CSRs that support medicine approvals available for public download as well as the proportion eligible for independent researcher request via the study sponsor. STUDY DESIGN AND SETTING:This cross-sectional study examined the accessibility of CSRs from industry-sponsored clinical trials whose results were reported in the FDA-authorized drug labels of the top 30 highest-revenue medicines of 2021. We determined (1) whether the CSRs were available for download from a public repository, and (2) whether the CSRs were eligible for request by independent researchers based on trial sponsors' data sharing policies. RESULTS:There were 316 industry-sponsored clinical trials with results presented in the FDA-authorized drug labels of the 30 sampled medicines. Of these trials, CSRs were available for public download from 70 (22%), with 37 available at EMA and 40 at Health Canada repositories. While pharmaceutical company platforms offered no direct downloads of CSRs, sponsors confirmed that CSRs from 183 (58%) of the 316 clinical trials were eligible for independent researcher request via the submission of a research proposal. Overall, 218 (69%) of the sampled clinical trials had CSRs available for public download and/or were eligible for request from the trial sponsor. CONCLUSION:CSRs were available from 69% of the clinical trials supporting regulatory approval of the 30 medicines sampled. However, only 22% of the CSRs were directly downloadable from regulatory agencies, the remaining required a formal application process to request access to the CSR from the study sponsor.

Importance With the growing use of large language models (LLMs) in education and health care settings, it is important to ensure that the information they generate is diverse and equitable, to avoid reinforcing or creating stereotypes that may influence the aspirations of upcoming generations. Objective To evaluate the gender representation of LLM-generated stories involving medical doctors, surgeons, and nurses and to investigate the association of varying personality and professional seniority descriptors with the gender proportions for these professions. Design, Setting, and Participants This is a cross-sectional simulation study of publicly accessible LLMs, accessed from December 2023 to January 2024. GPT-3.5-turbo and GPT-4 (OpenAI), Gemini-pro (Google), and Llama-2-70B-chat (Meta) were prompted to generate 500 stories featuring medical doctors, surgeons, and nurses for a total 6000 stories. A further 43 200 prompts were submitted to the LLMs containing varying descriptors of personality (agreeableness, neuroticism, extraversion, conscientiousness, and openness) and professional seniority. Main Outcomes and Measures The primary outcome was the gender proportion (she/her vs he/him) within stories generated by LLMs about medical doctors, surgeons, and nurses, through analyzing the pronouns contained within the stories using chi 2 analyses. The pronoun proportions for each health care profession were compared with US Census data by descriptive statistics and chi 2 tests. Results In the initial 6000 prompts submitted to the LLMs, 98% of nurses were referred to by she/her pronouns. The representation of she/her for medical doctors ranged from 50% to 84%, and that for surgeons ranged from 36% to 80%. In the 43 200 additional prompts containing personality and seniority descriptors, stories of medical doctors and surgeons with higher agreeableness, openness, and conscientiousness, as well as lower neuroticism, resulted in higher she/her (reduced he/him) representation. For several LLMs, stories focusing on senior medical doctors and surgeons were less likely to be she/her than stories focusing on junior medical doctors and surgeons. Conclusions and Relevance This cross-sectional study highlights the need for LLM developers to update their tools for equitable and diverse gender representation in essential health care roles, including medical doctors, surgeons, and nurses. As LLMs become increasingly adopted throughout health care and education, continuous monitoring of these tools is needed to ensure that they reflect a diverse workforce, capable of serving society's needs effectively.

Background Amid growing emphasis from pharmaceutical companies, advocacy groups, and regulatory bodies for sharing of individual participant data, recent audits reveal limited sharing, particularly for high-revenue medicines. Therefore, this study aimed to assess the individual participant data-sharing eligibility of clinical trials supporting the Food and Drug Administration approval of the top 30 highest-revenue medicines for 2021. Methods A cross-sectional analysis was conducted on 316 clinical trials supporting approval of the top 30 revenue-generating medicines of 2021. The study assessed whether these trials were eligible for individual participant data sharing, defined as being publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. Information was gathered from various sources including ClinicalTrials.gov, the European Union Clinical Trials Register, and PubMed. Key factors such as the trial phase, completion dates, and the nature of the data-sharing process were also examined. Results Of the 316 trials, 201 (64%) were confirmed eligible for sharing, meaning they were either publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. A total of 102 (32%) were confirmed ineligible, and for 13 (4%), the sponsor indicated that a full research proposal would be required to determine eligibility. The analysis also revealed a higher rate of individual participant data sharing among companies that utilized independent platforms, such as Vivli, for managing their individual participant data-sharing process. Trials not marked as completed had significantly lower eligibility for individual participant data sharing. Conclusion This study highlights that a substantial portion of trials for top revenue-generating medicines are eligible for individual participant data sharing. However, challenges persist, particularly for trials that are marked as ongoing and for trials where the sharing processes are managed internally by pharmaceutical companies. Data-sharing rates could be improved by adopting open-access individual participant data-sharing models or using independent platforms. Standardizing policies to facilitate immediate individual participant data availability for approved medicines is necessary.

OBJECTIVES To evaluate the effectiveness of safeguards to prevent large language models (LLMs) from being misused to generate health disinformation, and to evaluate the transparency of artificial intelligence (AI) developers regarding their risk mitigation processes against observed vulnerabilities. DESIGN Repeated cross sectional analysis. SETTING Publicly accessible LLMs. METHODS In a repeated cross sectional analysis, four LLMs (via chatbots/assistant interfaces) were evaluated: OpenAI's GPT-4 (via ChatGPT and Microsoft's Copilot), Google's PaLM 2 and newly released Gemini Pro (via Bard), Anthropic's Claude 2 (via Poe), and Meta's Llama 2 (via HuggingChat). In September 2023, these LLMs were prompted to generate health disinformation on two topics: sunscreen as a cause of skin cancer and the alkaline diet as a cancer cure. Jailbreaking techniques (ie, attempts to bypass safeguards) were evaluated if required. For LLMs with observed safeguarding vulnerabilities, the processes for reporting outputs of concern were audited. 12 weeks after initial investigations, the disinformation generation capabilities of the LLMs were re-evaluated to assess any subsequent improvements in safeguards.

Intravenous midazolam is frequently used for procedural sedation. Use of ritonavir containing antivirals in patients requiring procedural sedation with intravenous midazolam is postulated to increase the risk or prolong the consequences of exposure related adverse events. The primary objective of this study was to characterize interaction of ritonavir with IV midazolam. The secondary objective was to define the time course over with the interaction of ritonavir with IV midazolam resolves following cessation of ritonavir. Physiologically based pharmacokinetic modeling was used to conduct clinical trials with a parallel group design defining exposure to a single 5 mg IV dose of midazolam in the presence and absence of nirmatrelvir/ritonavir dosed twice daily for 5 days. Simulations comprised 50 virtual healthy subjects aged 20 to 50 years (50% female). Based on FDA criteria, a moderate/strong interaction between nirmatrelvir/ritonavir and intravenous midazolam (area under the curve [AUC] ratio >2) was observed when intravenous midazolam was administered up to 72 h following cessation of nirmatrelvir/ritonavir. The geometric mean (90% CI) midazolam AUC ratio was 9.21 (5.44 to 16.43) when coadministered on the final day of nirmatrelvir/ritonavir dosing. Importantly, there was no change in peak exposure; the geometric mean (90% CI) midazolam maximum concentration ratio was 0.99 (0.99 to 1.00). Use of ritonavir containing antivirals is unlikely to increase a patient's risk of experiencing an exposure related adverse event following administration of intravenous midazolam but may prolong complications in patients who experience an event. A meaningful interaction persists for 72 h following cessation of nirmatrelvir/ritonavir.

Background Multiple studies have indicated that patients with high body mass index (BMI) may have favourable survival outcomes following treatment with an immune checkpoint inhibitor (ICI). However, this evidence is limited by several factors, notably the minimal evidence from randomised controlled trials (RCTs), the use of categorised BMI with inconsistent cut point definitions, and minimal investigation of contemporary combination ICI therapy. Moreover, whether overweight and obese patients gain a larger benefit from contemporary frontline chemoimmunotherapy in non-small cell lung cancer (NSCLC) is unclear. Methods This secondary analysis pooled individual patient data from the intention-to-treat population of the IMpower130 and IMpower150 RCTs comparing chemoimmunotherapy versus chemotherapy. Co-primary outcomes were overall survival (OS) and progression-free survival (PFS). The potentially non-linear relationship between BMI and chemoimmunotherapy treatment effect was evaluated using Multivariable Fractional Polynomial Interaction (MFPI). As a sensitivity analysis, chemoimmunotherapy treatment effect (chemoimmunotherapy versus chemotherapy) on survival was also estimated for each BMI subgroup defined by World Health Organisation classification. Exploratory analyses in the respective chemoimmunotherapy and chemotherapy cohort were undertaken to examine the survival outcomes among BMI subgroups. Results A total of 1282 patients were included. From the MFPI analysis, BMI was not significantly associated with chemoimmunotherapy treatment effect with respect to either OS (p = 0.71) or PFS ( p = 0.35). This was supported by the sensitivity analyses that demonstrated no significant treatment effect improvement in OS/PFS among overweight or obese patients compared to normal weight patients (OS: normal BMI HR = 0.74 95% CI 0.59–0.93, overweight HR = 0.78 95% CI 0.61–1.01, obese HR = 0.84 95% CI 0.59–1.20). Exploratory analyses further highlighted that survival outcomes were not significantly different across BMI subgroups in either the chemoimmunotherapy therapy cohort (Median OS: normal BMI 19.9 months, overweight 17.9 months, and obese 19.5 months, p = 0.7) or the chemotherapy cohort (Median OS: normal 14.1 months, overweight 15.9 months, and obese 16.7 months, p = 0.7). Conclusion There was no association between high BMI (overweight or obese individuals) and enhanced chemoimmunotherapy treatment benefit in front-line treatment of advanced non-squamous NSCLC. This contrasts with previous publications that showed a superior treatment benefit in overweight and obese patients treated with immunotherapy given without chemotherapy.

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

AimsDrug exposure and response is determined by pharmacokinetic (PK) and pharmacodynamic (PD) profiles. Interindividual differences in abundance of drug metabolizing enzymes (DMEs) and drug target proteins underpin PK and PD variability and impact treatment efficacy and tolerability. Extracellular vesicles (EVs) carry protein cargo inherited from originating cells and may be useful for defining differences in key proteins related to hepatic drug metabolism and the treatment of metabolic‐associated fatty liver disease (MAFLD). We sought to quantify these proteins in liver‐derived EVs and establish the profile relative to paired tissue.MethodsEVs were recovered from human liver tissue samples (LT‐EV, n = 11). Targeted liquid chromatography with tandem mass spectrometry (LC–MS/MS) assays were employed for absolute quantification of proteins in EV isolates and matched liver tissue.ResultsDMEs and MAFLD drug targets were readily detected and quantified in LT‐EVs. Twelve of 15 DMEs exhibited moderate to strong correlation (Spearman ⍴ = 0.618–0.973) between tissue and EVs. Correlation in protein abundance was influenced by the extent of extra‐hepatic expression of the target.ConclusionsThis study provides evidence that key proteins related to PK and PD profiles can be measured in liver‐derived EVs and abundance of liver‐enriched DMEs are robustly correlated between paired tissue and EVs. The robust detection of protein markers related to drug PD profile in MAFLD opens the possibility to track within‐subject changes in MAFLD and lays the foundation for future development of a liver‐derived EV liquid biopsy to assess markers of drug exposure and response in vivo.

Abstract PF‐06835919, a ketohexokinase inhibitor, presented as an inducer of cytochrome P450 3A4 (CYP3A4) in vitro (human primary hepatocytes), and static mechanistic modeling exercises predicted significant induction in vivo (oral midazolam area under the plasma concentration‐time curve [AUC] ratio [AUCR] = 0.23–0.79). Therefore, a drug–drug interaction study was conducted to evaluate the effect of multiple doses of PF‐06835919 (300 mg once daily × 10 days; N = 10 healthy participants) on the pharmacokinetics of a single oral midazolam 7.5 mg dose. The adjusted geometric means for midazolam AUC and its maximal plasma concentration were similar following co‐administration with PF‐06835919 (vs. midazolam administration alone), with ratios of the adjusted geometric means (90% confidence interval [CI]) of 97.6% (90% CI: 79.9%–119%) and 98.9% (90% CI: 76.4%–128%), respectively, suggesting there was minimal effect of PF‐06835919 on midazolam pharmacokinetics. Lack of CYP3A4 induction was confirmed after the preparation of subject plasma‐derived small extracellular vesicles (sEVs) and conducting proteomic and activity (midazolam 1′‐hydroxylase) analysis. Consistent with the midazolam AUCR observed, the CYP3A4 protein expression fold‐induction (geometric mean, 90% CI) was low in liver (0.9, 90% CI: 0.7–1.2) and non‐liver (0.9, 90% CI: 0.7‐1.2) sEVs (predicted AUCR = 1.0, 90% CI: 0.9–1.2). Likewise, minimal induction of CYP3A4 activity (geometric mean, 90% CI) in both liver (1.1, 90% CI: 0.9–1.3) and non‐liver (0.9, 90% CI: 0.5‐1.5) sEVs was evident (predicted AUCR = 0.9, 90% CI: 0.6‐1.4). The results showcase the integrated use of an oral CYP3A probe (midazolam) and plasma‐derived sEVs to assess a drug candidate as inducer.

Unlocking the full potential of clinical trials through comprehensive CSR and IPD sharing can revolutionize cancer care, enhance safety evaluations, and reduce bias in systematic reviews. It is time for all stakeholders to embrace transparency and advance patient-centered outcomes.

The highly heterogenous nature of colorectal cancer can significantly hinder its early and accurate diagnosis, eventually contributing to high mortality rates. The adenoma-carcinoma sequence and serrated polyp-carcinoma sequence are the two most common sequences in sporadic colorectal cancer. Genetic alterations in adenomatous polyposis coli (APC), v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) and tumour protein 53 (TP53) genes are critical in adenoma-carcinoma sequence, whereas v-Raf murine sarcoma viral oncogene homolog B (BRAF) and MutL Homolog1 (MLH1) are driving oncogenes in the serrated polyp-carcinoma sequence. Sporadic mutations in these genes contribute differently to colorectal cancer pathogenesis by introducing distinct alterations in several signalling pathways that rely on the endosome-lysosome system. Unsurprisingly, the endosome-lysosome system plays a pivotal role in the hallmarks of cancer and contributes to specialised colon function. Thus, the endosome-lysosome system might be distinctively influenced by different mutations and these alterations may contribute to the heterogenous nature of sporadic colorectal cancer. This review highlights potential connections between major sporadic colorectal cancer mutations and the diverse pathogenic mechanisms driven by the endosome-lysosome system in colorectal carcinogenesis.