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The selection of appropriate starting dose and suitable method to predict an efficacious dose for novel oncology drug in the early clinical development stage poses significant challenges. The traditional methods of using body surface area transformation from toxicology studies to predict the first-in human (FIH) starting dose, or simply selecting the maximum tolerated dose (MTD) or maximum administered dose (MAD) as efficacious dose or recommended phase 2 dose (RP2D), are usually inadequate and risky for novel oncology drugs.Due to the regulatory efforts aimed at improving dose optimisation in oncology drug development, clinical dose selection is now shifting away from these traditional methods towards a comprehensive benefit/risk assessment-based approach. Quantitative pharmacology analysis (QPA) plays a crucial role in this new paradigm. This mini-review summarises the use of QPA in selecting the starting dose for oncology FIH studies and potential efficacious doses for expansion or phase 2 trials. QPA allows for a more rational and scientifically based approach to dose selection by integrating information across studies and development phases.In conclusion, the application of QPA in oncology drug development has the potential to significantly enhance the success rates of clinical trials and ultimately support clinical decision-making, particularly in dose selection.

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Multiple international guidelines exist that describe both quality and safety considerations for the control of the broad spectrum of impurities inherent to drug substance and product manufacturing processes. However, regarding non-mutagenic impurities (NMI) the most relevant ICH Q3A/B guidelines are not applicable during early phases of drug development leading to confusion about acceptable limits at this stage. Thus, there is need for more flexible approaches that ensure that patient safety remains paramount, while taking into consideration the limited duration of exposure. An EFPIA survey, which collected quantitative data from different types of studies applied to qualify impurities in accordance with ICH Q3A, shows that no toxicities could be attributed to any of the 467 impurities at any tested level in vivo. This data combined with earlier published toxicological datasets encompassing drug substances and intermediates, food related substances and chemicals provide convincing evidence that for NMIs, the application of a generic 5 mg/day limit for an exposure duration <6 months, and a 1 mg/day generic limit for life-long exposure, provides sufficient margins to ensure patient safety. Hence, application of these absolute limits to trigger qualification studies (instead of the relative limits described in Q3A/B), is considered warranted. This approach will prevent conduct of unnecessary dedicated impurity qualification studies and the resulting use of animals.

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BACKGROUND: It has been recognized that patients should be involved in the design of clinical trials. However, there is a lack of agreement on what patient-centricity means. METHODS: In this article, a Patient Motivation Pyramid based on Maslow's theory of human motivation is introduced as a tool to identify patient needs. This pyramid is used to make a comprehensive overview of options to implement a patient-centric trial design. The Pyramid with the described options can help to identify patient-centric activities suitable for given drug development. The current article further describes the potential benefits of patient-centric trial designs with an emphasis on early clinical development. Especially in early clinical development, during which trials have many assessments per patient, and the safety and clinical efficacy are uncertain, patient-centric trial design can improve feasibility. Finally, we present three case examples on patient-centric trial design. The first example is seeking patient input on the trial design for a First-in-Human trial which includes patients with Immune Thrombocytopenic Purpura. The second example is the use of a video-link for home dosing. The final example is the use of digital medicine in a decentralized trial in heart failure patients. RESULTS: A comprehensive overview of patients' needs can be accomplished by building a Patient Motivation Pyramid as a tool. Patient input can lead to improved endpoints, improved feasibility, better recruitment, less dropout, less protocol amendments, and higher patient satisfaction. The use of digital medicine can lead to a trial design with much less visits to the clinical research center in early clinical development and in a later development phase, even to a complete virtual trial. CONCLUSION: We recommend using the Patient Motivation Pyramid as a structural approach for identifying elements of patient-centricity. Secondly, we recommend starting using patient-centric approaches in an early phase of the medicine's lifecycle.

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To support further development of model-informed drug development approaches leveraging circulating tumor DNA (ctDNA), we performed an exploratory analysis of the relationships between treatment-induced changes to ctDNA levels, clinical response and tumor size dynamics in patients with cancer treated with checkpoint inhibitors and targeted therapies. This analysis highlights opportunities for pharmacometrics approaches such as for optimizing sampling design strategies. It also highlights challenges related to the nature of the data and associated variability overall emphasizing the importance of mechanistic modeling studies of the underlying biology of ctDNA processes such as shedding, release and clearance and their relationships with tumor size dynamic and treatment effects.

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AIMS: We aimed to incorporate a pharmacologically inactive midazolam microdose into early clinical studies for the assessment of CYP3A drug-drug interaction liability. METHODS: Three early clinical studies were conducted with substances (Compounds A, B and C) which gave positive CYP3A perpetrator signals in vitro. A 75 µg dose of midazolam was administered alone (baseline CYP3A activity) followed by administration with the highest dose groups tested for each compound on Day 1/3 and Day 14 or Day 17. Midazolam exposure (AUC0-∞ , Cmax ) during administration with the test substances was compared to baseline data via an analysis of variance on log-transformed data. Partial AUC2-4 ratios were also compared to AUC0-∞ ratios using linear regression on log-transformed data. RESULTS: Test compound Cmax values exceeded relevant thresholds for drug-drug interaction liability. Midazolam concentrations were quantifiable over the full profiles for all subjects in all studies. Point estimates of the midazolam AUC0-∞ gMean ratios ranged from 108.3 to 127.1% for Compound A, from 93.3 to 114.5% for Compound B, and from 92.0 to 96.7% for the two highest dose groups of Compound C. Cmax gMean ratios were in the same range. Thus, no relevant drug-drug interactions were evident, based on the results of midazolam microdosing. AUC2-4 ratios from these studies were comparable to the AUC0-∞ ratios. CONCLUSION: Midazolam microdosing incorporated into early clinical studies is a feasible tool for reducing dedicated drug-drug interaction studies, meaning reduced subject burden. Limited sampling could further reduce subject burden, costs and needed resources.

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The principles of tiered approach have been part of the bioanalytical toolbox for some years. Nevertheless, an in spite of many valuable discussions in industry, they remain difficult to apply in a harmonized way for a broad array of studies in early drug development where these alternative approaches to regulated validation would make sense. The European Bioanalysis Forum has identified the need to proposes some practical workflows for five categories of studies for chromatography based assays where scientific validation will allow additional freedom while safeguarding scientific rigor and robust documentation: quantification of metabolites in plasma in relation to ICH M3(R2), urine analysis, tissue homogenate analysis, and preclinical and clinical studies in early stages of drug development. The recommendation would introduce a common language and harmonized best practice for these study categories and can help to refocus towards optimized scientific and resource investments for bioanalysis in early drug development.

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Probability of success in phase II dominates the drug development cost calculus, with phase I/II as the critical juncture for proof of concept. Failure to address fundamental pharmacologic questions in early development is alarmingly frequent and a strong predictor of failure. Safety, manufacture, formulation, and commercialization issues are also vital. Systems biology provides a framework to analyze genomic, proteomic, and metabolomic data and construct complex network models of molecular pathophysiology. Biomarkers offer the largest learning opportunity, and combined adaptive protocol designs provide a lean but scientifically robust path to proof of concept. The traditional model of phase I study execution in a clinical pharmacology unit is evolving to a networked model of an integrated early clinical development platform. The power of this platform is enhanced with a proactive multidisciplinary approach to quality and safety, including lean 6 sigma tools and simulations.