Quotient Sciences Unveils AI-Driven Approach to Optimizing Drug Formulations
In a significant advancement in drug formulation, Quotient Sciences, a prominent integrated Contract Research, Development and Manufacturing Organization (CRDMO), has recently shared interim results from a clinical study employing its proprietary artificial intelligence (AI) algorithm. This innovative approach allowed for the selection of optimal modified-release formulations and dosing levels in a study conducted with healthy participants. The preliminary results indicate a remarkable potential for the algorithm to facilitate the development of optimized drug formulations with fewer clinical rounds than traditional methods.
The clinical study aimed to meet preset pharmacokinetic targets, which the algorithm successfully achieved within just three dosing periods, showcasing its abilities in swiftly navigating the complex relationship between tablet composition and drug performance in humans. Andrew Lewis, Ph.D., Chief Scientific Officer at Quotient Sciences, highlighted the challenges of predicting the behavior of modified-release tablets in human subjects, noting the algorithm's efficiency in learning and reaching the desired pharmacokinetic profile with surprising accuracy.
Typically, achieving such targeted profiles through traditional formulation development necessitates multiple iterations and extensive testing, often consuming months or even years. However, the AI-driven solution changes this landscape, accelerating the process by learning relationships between formulation variables and outcomes at a much faster pace. Lewis remarked, 'The interim data show that the algorithm learned that relationship quickly and accurately, reaching our preset target within three dosing periods.' This rapid learning capability is not only impressive but suggests a transformative shift in how drug formulations can be developed in the future.
Prior to the clinical study, laboratory screening demonstrated the algorithm's potential by learning the relationship between tablet composition and in vitro drug release effectively. This preclinical success led to the hypothesis that the model could also correlate composition to pharmacokinetics in human subjects. The algorithm entered the trial with training solely based on in vitro release data, and after each dosing phase, it was retrained using dissolution results and pharmacokinetic data gathered from participants. Quotient Sciences established specific parameters for the algorithm to operate within, including caps on doses for initial prototypes, while a safety committee ensured that every composition was approved prior to manufacture and dosing, emphasizing the organization's commitment to human safety throughout the trial.
For the clinical evaluation, a generic drug was strategically chosen due to its well-established safety profile and extensive published data, not as a potential product for development. This careful selection underscores the aim of the study: to investigate whether the model could accurately learn and predict the relationship between formulation composition and human performance.
Quotient Sciences is optimistic about the findings so far, with Lewis indicating that if these initial signs continue to hold true, the future of drug development could see significantly reduced clinical testing times for modified-release formulations of various molecules. The ongoing dosing work will be monitored closely, with comprehensive data set to be reported upon the conclusion of the study later this year.
This groundbreaking work enhances Quotient Sciences’ already established Translational Pharmaceutics® platform, which integrates aspects of drug product development, manufacturing, and clinical testing into one cohesive unit. The AI-enhanced solution thus supports a model-informed approach to drug development, helping to create digital twins that link formulation composition with in vitro performance and human pharmacokinetics. For drug developers wishing to leverage AI-enhanced formulation strategies for their early-stage programs, Quotient Sciences encourages contacting their dedicated scientific team for guidance.
With over two decades of experience, Quotient Sciences stands at the forefront of innovative drug development. Their Translational Pharmaceutics® platform, coupled with cutting-edge AI insights, empowers sponsors – from emerging biotech entities to Fortune 50 pharmaceutical giants – to improve decision-making early in the development cycle, thereby accelerating pathways to proof-of-concept studies while minimizing both time and cost risks. This combination of advanced technology and extensive experience clearly positions Quotient Sciences as a leader in the evolving field of drug formulation and development.
The clinical study aimed to meet preset pharmacokinetic targets, which the algorithm successfully achieved within just three dosing periods, showcasing its abilities in swiftly navigating the complex relationship between tablet composition and drug performance in humans. Andrew Lewis, Ph.D., Chief Scientific Officer at Quotient Sciences, highlighted the challenges of predicting the behavior of modified-release tablets in human subjects, noting the algorithm's efficiency in learning and reaching the desired pharmacokinetic profile with surprising accuracy.
Typically, achieving such targeted profiles through traditional formulation development necessitates multiple iterations and extensive testing, often consuming months or even years. However, the AI-driven solution changes this landscape, accelerating the process by learning relationships between formulation variables and outcomes at a much faster pace. Lewis remarked, 'The interim data show that the algorithm learned that relationship quickly and accurately, reaching our preset target within three dosing periods.' This rapid learning capability is not only impressive but suggests a transformative shift in how drug formulations can be developed in the future.
Prior to the clinical study, laboratory screening demonstrated the algorithm's potential by learning the relationship between tablet composition and in vitro drug release effectively. This preclinical success led to the hypothesis that the model could also correlate composition to pharmacokinetics in human subjects. The algorithm entered the trial with training solely based on in vitro release data, and after each dosing phase, it was retrained using dissolution results and pharmacokinetic data gathered from participants. Quotient Sciences established specific parameters for the algorithm to operate within, including caps on doses for initial prototypes, while a safety committee ensured that every composition was approved prior to manufacture and dosing, emphasizing the organization's commitment to human safety throughout the trial.
For the clinical evaluation, a generic drug was strategically chosen due to its well-established safety profile and extensive published data, not as a potential product for development. This careful selection underscores the aim of the study: to investigate whether the model could accurately learn and predict the relationship between formulation composition and human performance.
Quotient Sciences is optimistic about the findings so far, with Lewis indicating that if these initial signs continue to hold true, the future of drug development could see significantly reduced clinical testing times for modified-release formulations of various molecules. The ongoing dosing work will be monitored closely, with comprehensive data set to be reported upon the conclusion of the study later this year.
This groundbreaking work enhances Quotient Sciences’ already established Translational Pharmaceutics® platform, which integrates aspects of drug product development, manufacturing, and clinical testing into one cohesive unit. The AI-enhanced solution thus supports a model-informed approach to drug development, helping to create digital twins that link formulation composition with in vitro performance and human pharmacokinetics. For drug developers wishing to leverage AI-enhanced formulation strategies for their early-stage programs, Quotient Sciences encourages contacting their dedicated scientific team for guidance.
With over two decades of experience, Quotient Sciences stands at the forefront of innovative drug development. Their Translational Pharmaceutics® platform, coupled with cutting-edge AI insights, empowers sponsors – from emerging biotech entities to Fortune 50 pharmaceutical giants – to improve decision-making early in the development cycle, thereby accelerating pathways to proof-of-concept studies while minimizing both time and cost risks. This combination of advanced technology and extensive experience clearly positions Quotient Sciences as a leader in the evolving field of drug formulation and development.
Topics Health）
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