Model Informed Drug Development and Regulation in China: Challenges and Opportunities

Li, Li; Han, Hongcan; Wang, Jun; Wei, Chen; Wang, Yuzhu; Li, Min; Zhou, Yu; Yang, Jinbo

doi:10.1002/psp4.12368

Cited by 14 publications

(12 citation statements)

References 5 publications

(11 reference statements)

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“…As the concept of model-informed drug development (MIDD) is gaining higher recognition on the back of several regulatory [1][2][3][4] and industrial initiatives, 5,6 the debate on the nature of the modeling platforms and associated databases that constitute a "model" is heating up.…”

Section: Introductionmentioning

confidence: 99%

Opening a debate on open‐source modeling tools: Pouring fuel on fire versus extinguishing the flare of a healthy debate

Rostami‐Hodjegan

2021

CPT Pharmacom & Syst Pharma

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Section: Introductionmentioning

confidence: 99%

Opening a debate on open‐source modeling tools: Pouring fuel on fire versus extinguishing the flare of a healthy debate

Rostami‐Hodjegan

2021

CPT Pharmacom & Syst Pharma

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“…Modeling and simulation might be an approach to address the lack in representativity of historical data if forecasts of disease transmission can be joined with clinical trial simulation. For example, simulated clinical trials have provided the means to test a multitude of design choices 27–30 and became a field gaining attraction throughout regulatory agencies 31 . The COVID-19 pandemic has already transformed the modeling and simulation community.…”

Section: Introductionmentioning

confidence: 99%

Viral kinetic modeling and clinical trial simulation predicts disruption of respiratory disease trials by non-pharmaceutical COVID-19 interventions

Arsène¹,

Couty²,

Faddeenkov³

et al. 2021

Preprint

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Clinical research in infectious respiratory diseases has been profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19. On top of trial delays or even discontinuation which have been observed in all disease areas, NPIs altered transmission pattern of many seasonal respiratory viruses which followed regular patterns for decades before the pandemic. Clinical trial design based on pre-pandemic historical data therefore needs to be put in question. In this article, we show how knowledge-based mathematical modeling can be used to address this issue. We set up an epidemiological model of respiratory tract infection (RTI) sensitive to a time dependent between-host transmission rate and coupled this model to a mechanistic description of viral RTI episodes in an individual patient. By reducing the transmission rate when the lockdown was introduced in the United Kingdom in March 2020, we were able to reproduce the perturbed 2020 RTI disease burden data. Using this setup, we simulated several NPIs scenarios of various strength (none, mild, medium, strong) and conducted placebo-controlled in silico clinical trials in pediatric patients with recurrent RTIs (RRTI) quantifying annual RTI rate distributions. In interventional arms, virtual patients aged 1-5 years received the bacterial lysate OM-85 (approved in several countries for the prevention of pediatric RRTIs) through a pro-type I immunomodulation mechanism of action described by a physiologically based pharmacokinetics and pharmacodynamics approach (PBPK/PD). Our predictions showed that sample size estimates based on the ratio of RTI rates (or the post-hoc power of fixed sample size trials) are not majorly impacted under NPIs which are less severe (none, mild and medium NPIs) than a strict lockdown (strong NPI). However, NPIs show a stronger impact on metrics more relevant for assessing the clinical relevance of the effect such as absolute benefit. This dichotomy shows the risk that successful trials (even with their primary endpoints being met) still get challenged in risk benefit assessment during the review of market authorization. Furthermore, we found that a mild NPI scenario already affected the time to recruit significantly when sticking to eligibility criteria complying with historical data. In summary, our model predictions can help rationalize and forecast post-COVID-19 trial feasibility. They advocate for gauging absolute and relative benefit metrics as well as clinical relevance for assessing efficacy hypotheses in trial design and they question eligibility criteria misaligned with the actual disease burden.

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“…Many aspects of clinical pharmacology have become more quantitative, as mathematical modeling and simulation is considered to be an integral part of the field and is increasingly used in drug development, described as Model Informed Drug Development or pharmacometrics ( Wang et al, 2019 , Marshall et al, 2016 , Visser et al, 2013 ). Model Informed Drug Development is used to leverage the data gained from different sources, including real world-data, clinical studies, and preclinical studies to assist in decision making during drug development ( Wang et al, 2019 , Visser et al, 2013 , Marshall et al, 2019 , Li et al, 2019 , Huang et al, 2013 ). Model Informed Drug Development is part of the United States (US) FDA’s goals in the Prescription Drug User Fee Amendments of 2017 ( https://www.fda.gov/drugs/news-events-human-drugs/cder-conversation-model-informed-drug-development ).…”

Section: Introductionmentioning

confidence: 99%

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Alsultan

Alghamdi

et al. 2020

Saudi Pharmaceutical Journal

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Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

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Model Informed Drug Development and Regulation in China: Challenges and Opportunities

Cited by 14 publications

References 5 publications

Opening a debate on open‐source modeling tools: Pouring fuel on fire versus extinguishing the flare of a healthy debate

Opening a debate on open‐source modeling tools: Pouring fuel on fire versus extinguishing the flare of a healthy debate

Viral kinetic modeling and clinical trial simulation predicts disruption of respiratory disease trials by non-pharmaceutical COVID-19 interventions

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

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