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Purpose To mechanistically study and model the effect of lipids, either from food or self-emulsifying drug delivery systems (SEDDS), on drug transport in the intestinal lumen. Methods Simultaneous lipid digestion, dissolution/release, and drug partitioning were experimentally studied and modeled for two dosing scenarios: solid drug with a food-associated lipid (soybean oil) and drug solubilized in a model SEDDS (soybean oil and Tween 80 at 1:1 ratio). Rate constants for digestion, permeability of emulsion droplets, and partition coefficients in micellar and oil phases were measured, and used to numerically solve the developed model. Results Strong influence of lipid digestion on drug release from SEDDS and solid drug dissolution into food-associated lipid emulsion were observed and predicted by the developed model. 90 minutes after introduction of SEDDS, there was 9% and 70% drug release in the absence and presence of digestion, respectively. However, overall drug dissolution in the presence of food-associated lipids occurred over a longer period than without digestion. Conclusion A systems-based mechanistic model incorporating simultaneous dynamic processes occurring upon dosing of drug with lipids enabled prediction of aqueous drug concentration profile. This model, once incorporated with a pharmacokinetic model considering processes of drug absorption and drug lymphatic transport in the presence of lipids, could be highly useful for quantitative prediction of impact of lipids on bioavailability of drugs.

Active targeted delivery of nanoparticle-encapsulated agents to tumor cells in vivo is expected to enhance therapeutic effect with significantly less non-specific toxicity. Active targeting is based on surface modification of nanoparticles with ligands that bind with extracellular targets and enhance payload delivery in the cells. In this study, we have used label-free Raman micro-spectral analysis and kinetic modeling to study cellular interactions and intracellular delivery of C6-ceramide using a non-targeted and an epidermal growth factor receptor (EGFR) targeted biodegradable polymeric nano-delivery systems, in EGFR-expressing human ovarian adenocarcinoma (SKOV3) cells. The results show that EGFR peptide-modified nanoparticles were rapidly internalized in SKOV3 cells leading to significant intracellular accumulation as compared to non-specific uptake by the non-targeted nanoparticles. Raman micro-spectral analysis enables visualization and quantification of the carrier system, drug-load, and responses of the biological systems interrogated, without exogenous staining and labeling procedures.

Emulsion based drug delivery systems have shown great promise for enhancing oral bioavailability yet have not been widely commercially utilized, largely due to lack of mechanistic understanding of their function. Major functional properties of emulsion-based drug delivery systems, permeability enhancement and drug release, were studied and statistically related to a broad range of formulation properties through Analysis of Variance (ANOVA) and regression analysis. Three surfactants with a high, medium and low Hydrophilic Lipophilic Balance (HLB) value and three structurally different oils (long chain triglyceride, medium chain triglyceride and propylene glycol dicaprylate/dicaprate) were combined at three oil to surfactant ratios. Heterogeneous formulations of low HLB (HLB=10) surfactant had a toxic effect on cells at high (>50%) surfactant concentrations, which may be an indication of importance of formulation stability for decreasing toxicity. Electrical resistance indicated that high HLB surfactant, Tween 80, loosens tight junction at high (>50%) surfactant concentrations. Release coefficients from each emulsion system were calculated. Incorporation of long chain triglyceride, Soybean oil, as the oil phase altered rate of release from oil droplets whereas high surfactant demonstrated a retarding effect.

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Quantitative characterization of pharmacokinetics (PK) and pharmacodynamics (PD) of drugs is an essential component in pharmacology/toxicology education as well as drug discovery/development. PK determines the relationship between the dose of the administered drug and the concentration measured in the body, whereas PD characterizes the extent of pharmacological effect induced by drug concentrations. Simulation and modeling of such properties is a powerful tool to predict drug's efficacy and toxicity with the limited dose‐concentration‐response data. Several software tools, such as WinNonlin, Simcyp, GastroPlus and SAAM II, are traditionally used for PK/PD modeling and simulation. However, these tools have not demonstrated the dynamic flexibility in the generation of novel modeling approaches in both academic and research settings. While MATLAB has been extensively used in the field of Science and Engineering with a variety of applications, SimBiology provides an app to model, simulate, and analyze dynamic systems, focusing on PK/PD and quantitative systems pharmacology (QSP) applications. It also provides a block diagram editor for building models, or the user can create models programmatically using the MATLAB language. Moreover, SimBiology includes a library of common PK models, which one can customize and integrate with mechanistic systems biology models. Consequently, MATLAB/SimBiology provides a comprehensive tool that can help to train young scientists in the field of pharmaceutical sciences, including pharmacology and toxicology. While our graduate program at Northeastern University offers several important features of fundamental PK/PD principles and advanced PK/PD analyses, an opportunity to have a hands‐on experience on PK/PD simulation and modeling has not been provided. In Spring 2016, we incorporated SimBiology as a modeling and simulation tool to our graduate Advanced Pharmacokinetics and Toxicology course through a collaboration with MathWorks. We then assessed the benefits of SimBiology in students' learning based on class participation and problem sets/exams, as well as students' survey through Teacher Rating and Course Evaluation (TRACE). The results demonstrated that SimBiology improved students' learning in several TRACE sections, including “The classroom technology helped me to learn”, “I found this course intellectually challenging”, and “I learned a lot in this course”. Our results also indicate the benefits and importance of hands‐on experience in modeling software to better understand PK/PD in pharmacology.Support or Funding InformationThis project was supported by MathWorks Curriculum Development Grant.