Delta-9-tetrahydrocannabinol (THC), the primary psychoactive component of cannabis, impairs visual working memory, essential for decision-making. However, the relationship between THC dose and working memory changes remains largely unexplored, especially in older adults who often face cognitive decline. Understanding how different THC doses and administration routes affect working memory is vital for public health. We have developed models to capture THC brain/plasma concentration profiles following various doses via different routes through secondary data analysis. Data reuse is crucial for exploring THC-induced working memory impairment. We will quantify brain exposure and its effects on working memory using data from the GREI repository and other published sources. This study aims to be the first to analyze the connection between THC dosing and working memory changes in young and older adults, which could inform policies on THC use limits in setting where working memory is critical
This study aims to develop a mathematical model linking THC dose to changes in working memory and simulate impairment across different doses in young and older adults. We will collect training and verification data from the GREI repository in the OSF and relevant PubMed publications. Specifically, we will analyze individual data from two GREI experiments on THC dosing and working memory performance, with additional data on THC doses and working memory impairment from PubMed.
We have developed a pharmacokinetics (PK) model using the physiologically based pharmacokinetic (PBPK) method, which integrates systems biology with mathematical equations to provide a mechanistic approach for analyzing populations that are challenging to represent in clinical trials. This model accurately captures THC brain/plasma concentration profiles following various doses via routes such as IV, oral, inhaled, and oromucosal spray in both young and older adults using secondary data from PubMed (manuscript is under review).
PBPK/PBPK-PD modeling and simulation will be conducted using the Simcyp™ PBPK Simulator (v23). Data management and cleaning will be performed using R software. This project will be carried out as below:
Specific Aim 1 (1 month): Generate a comprehensive database for THC and its effects on working memory impairment in clinical trials.
SA.1A: Collect relevant data from the GREI repository and PubMed studies related to THC dosing via various routes (IV, oral, inhaled, and oromucosal spray) and working memory.
SA.1B: Identify the most suitable datasets for modeling based on the data collected in SA.1A.
SA.1C: Extract and digitize data identified in SA.1B, including THC doses, concentrations, and measures of working memory performance.
Specific Aim 2 (3 months): Quantify the effects of THC exposure on changes in young adults' working memory.
SA.2A: Divide the collected studies into two groups: a training group for developing the model and a verification group for testing the model’s robustness based on the THC doses and routes of administration.
SA.2B: Develop mathematical models that capture how THC doses relate to brain exposure and changes in working memory (PBPK-PD model) using the training group from SA.2A.
SA.2C: Verify the models from SA.2B by comparing its predictions of THC concentration and working memory changes after different doses with observed data in the verification group.
Specific Aim 3 (1 month): Quantify the effects of THC on working memory in both young and older adults across different doses via different routes of administration.
SA.3A: Gather average working memory performance data from the literature for young and older adults to develop mathematical models (PD models) that capture THC dose responses in older adults.
SA.3B: Combine our established PK models for older adults with the model from SA3A (PBPK-PD model) to simulate the effects of various THC doses and administration routes on working memory performance in both age groups.
Our study will establish a quantitative dose-response relationship between THC doses, THC concentration in the brain, and changes in working memory. With the established PBPK-PD model, we will simulate the changes in working memory in young and older adults after different THC doses and routes of administration, including IV, oral, inhaled, and oromucosal spray. Dr. Zhu Zhou is a leader in several professional societies (American Society for Clinical Pharmacology and Therapeutics (ASCPT), American Association of Pharmaceutical Scientist and American Chinese Pharmaceutical Association). She has co-chaired more than 10 sessions at international and national Meetings and has co-chaired more than 7 webinars for different professional soceities. She has invited as speakers on related cannabis research in older adults in various national and international conferences. We could share our in the conferences and webinars.
In addition to publishing in journals, we will disseminate results via social media platforms like Twitter, LinkedIn, and Facebook. Our data sharing will adhere to FAIR principles:
With this, researchers can reproduce our study with the same software and use our model to predict and understand the possible working memory impairment in specific populations. Where relevant, we will also address CARE principles by ensuring that our research respects the rights and welfare of individuals, particularly older adults, who may be affected by THC exposure. This includes transparent reporting of the population studied and attention to ethical considerations throughout our research.
By implementing these principles, we aim to foster collaboration and enhance the impact of our findings, enabling researchers to use our model to predict and understand potential working memory impairment in specific populations.
Delta-9-tetrahydrocannabinol (THC) is the primary psychoactive component of cannabis, known to impair working memory—a crucial function that guides decision-making and behavior. This impairment has significant public health implications, particularly as cannabis-related traffic accidents have surged, with incidents requiring treatment increasing by 475% between 2010 and 2021. Therefore, quantitatively understanding the relationship between THC doses and changes in working memory is essential for assessing the heightened risk of traffic accidents and is critical for public health and safety.
Despite its importance, the relationship between THC dose and working memory changes remains largely unexplored. This gap is particularly concerning for older adults, who often face cognitive decline. As of January 2024, there are 62 million Americans aged 65 and older, comprising 18% of the population. Many in this group experience age-related comorbidities, leading to a rising trend in cannabis use to alleviate various ailments; in fact, cannabis use among older adults surged by approximately 170% from 2016 to 2022. Understanding how different THC doses and administration routes affect working memory in this demographic is vital for public health.
Current clinical trials have several limitations that contribute to these knowledge gaps: only a limited number of doses and routes of administration have been studied, the effects of THC on working memory have not been assessed over its full duration of action, and the specific impact of THC on working memory in older adults remains unknown. Given that many older adults already experience a decline in working memory, additional impairment from THC could be particularly detrimental.
Our modeling and simulation analysis, leveraging data reuse, will quantify the relationship between THC doses and changes in working memory. To our knowledge, this study will be the first to use modeling and simulation to assess the mathematical relationship between THC dose and working memory changes. The simulations will provide time-response curves for various THC doses and routes of administration, facilitating a better understanding of THC's effects, especially in older adults, when conducting clinical trials is challenging.
Furthermore, our modeling and simulation methods can be adapted to other cannabinoids and psychoactive substances that may also impair working memory. This study has the potential to directly inform policy-making regarding legal THC limits for drivers and in workplace settings where working memory is critical. Additionally, it will generate essential information for future research on driving safety among cannabis users across different age groups.
Dr. Zhu Zhou is a tenured Associate Professor at York College and Graduate Center, City University of New York. Her current research focuses on using mathematical and statistical modeling such as PBPK, PK/PD to assess drug disposition/response in older adults. Her lab is currently funded by the NIH on research related to THC and cannabidiol in older adults. She is a reviewer for various journals and serves as a member for NIH DMPB study sections. She is the recipient of the 2022 National Institute on Aging Butler-Williams Scholar Award and the 2023 American Society for Clinical Pharmacology and Therapeutics (ASCPT) Darrell Abernethy Early Stage Investigator Award.
Dr. Lixuan Qian is now a postdoc for Dr. Zhou at the City University of New York, York College. He has a PhD. degree in clinical pharmacy. Dr. Qian has more than 5 years of working experience in PK and PD modeling. His research uses mathematical and statistical modeling to guide clinical trial design, optimal sampling, and exposure-response analysis. His current projects as a postdoc are focused on modeling and simulation of THC and cannabidiol. The manuscript of mathematical models to describe the THC dose-concentration relationship is currently under review.
Our team has extensive expertise in PK and PD modeling. Dr. Zhou is a pioneer in quantifying the impact of age on THC and CBD exposure and has been invited to speak at numerous conferences. Dr. Qian has supported three successful global drug applications through modeling.
We have developed robust THC models in healthy adults via various routes of administrations. This foundation is essential for modeling working memory impairment. Additionally, our team has experience in modeling THC and other cannabinoids in older adults, providing valuable insights into age-related effects.
Recently, we have been establishing mathematical models for various psychoactive effects of THC, such as the sensation of being "high" and impairment of alertness (manuscripts in preparation), which share similar mechanisms with working memory impairment. Our combined professional skills in PK, PD, and modeling will enable us to advance this project successfully, ensuring rigorous analysis and meaningful results.
