SBIR/STTR Research Award

Rationally designed lipid- and food-based drug formulations to enhance oral bioavailability

Award identifier 1a27fc40bdd1ca5894166817ec58f11b3c8a377ec9ac5c41b5df0a769f3d7843Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$251,316

Program and phase

SBIR · Phase I

Award year

2021

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Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
MECHASIM INC.
Proposal award date
2021-09-27
Contract end date
2022-03-26
UEI
S6JEPLMAKH96
Solicitation number
PA19-272
Topic code
400

Research abstract

Abstract More than 40% of marketed drugs and about 90% of drugs in development pipelines suffer from poor aqueous solubility and associated low bioavailability. Food, and lipids in particular, can significantly enhance the bioavailability of such drugs. This “food effect” could be exploited by developing lipid- and food-based formulations that demonstrate enhanced efficiency absorption, and as a result can be designed at a reduced drug dose while maintaining the same systemic exposure. Notably, using food components as formulation excipients would also mitigate food effects on bioavailability, thus allowing patients to take these medications irrespective of food, reducing the complexity of medication regiments, and ultimately addressing the significant issue of low patient adherence. However, lipid and food use in oral drug delivery is limited, in part, due to lack of ability to predict a priori lipid function in the GI tract and its effect on drug absorption. Our team has developed an algorithm that can predict the impact of food quantity and composition on drug absorption and bioavailability from a mechanistic point of view. Specifically, our algorithm considers multiple parallel, dynamic processes (drug dissolution, lipid and protein digestion, drug partitioning in colloids, absorption) to quantitatively predict the impact of food-associated lipids and proteins on oral bioavailability. This approach offers tremendous value by enabling rational quantitative guidance in developing improved dosage forms using lipids and other food ingredients as formulation excipients. In this project, we aim to develop novel formulations of a poorly bioavailable oral marketed drug that is known to be significantly impacted by food and is restricted to be taken only after a meal. The novel dosage forms will demonstrate enhanced capabilities in that they can be administered at a lower dose for the same therapeutic effect and administration with or without food will not impact bioavailability. Development of these formulations will demonstrate the practical application of our algorithm to improve oral delivery of poorly bioavailable drugs by exploiting the food effect. In the first aim, the existing algorithm will be adapted to predict drug pharmacokinetics after administration of an aqueous lipid/protein emulsion-based delivery vehicle. Kinetic parameters will be measured in vitro and used as model input parameters to inform selection of the most optimal drug dose and lipid-protein mixture composition that is bioequivalent to the marketed formulation. In the second aim, the modeling framework will be employed to select a lipid-surfactant-cosolvent system that can be administered as a lipid-based formulation inside a capsule. Formulation parameters (e.g., drug dose, excipient composition) will be optimized to inform development of a formulation that is bioequivalent to the marketed dosage form. Bioequivalence among all formulations will be demonstrated via pharmacokinetic studies in dogs. Successful completion of these aims will demonstrate that our model can 1) effectively inform rational design of novel food- and lipid-based formulations with enhanced bioavailability, and 2) can allow development of lower dose formulations while maintaining the same exposure and therapeutic effect as the marketed dosage form.Project Narrative Food, and lipids in particular, can significantly impact bioavailability of many oral drugs, but lack of ability to predict the food effect a priori limits their application. We have developed a mechanistic computational model that can predict the impact of food ingredients (e.g., lipids, proteins) on oral drug absorption and bioavailability. The proposed work aims to employ this model to use lipids and other food components as formulation excipients and rationally design oral drug formulations that are superior to marketed dosage forms in that they can be taken at a reduced dose (thus reducing potential GI side effects) and that can be taken irrespective of food.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

2 SBIR/STTR records share this UEI (2021). Reported award amounts total $476,077. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: Department of Health and Human Services, National Science Foundation

Agency intelligence

Department of Health and Human Services

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