Clinical translation of advanced colonic drug delivery technologies.

Targeted drug delivery to the colon offers a myriad of benefits, including treatment of local diseases, direct access to unique therapeutic targets and the potential for increasing systemic drug bioavailability and efficacy. Although a range of traditional colonic delivery technologies are available, these systems exhibit inconsistent drug release due to physiological variability between and within individuals, which may be further exacerbated by underlying disease states. In recent years, significant translational and commercial advances have been made with the introduction of new technologies that incorporate independent multi-stimuli release mechanisms (pH and/or microbiota-dependent release). Harnessing these advanced technologies offers new possibilities for drug delivery via the colon, including the delivery of biopharmaceuticals, vaccines, nutrients, and microbiome therapeutics for the treatment of both local and systemic diseases. This review details the latest advances in colonic drug delivery, with an emphasis on emerging therapeutic opportunities and clinical technology translation.

Quantification of P-Glycoprotein in the Gastrointestinal Tract of Humans and Rodents: Methodology, Gut Region, Sex, and Species Matter.

Intestinal efflux transporters affect the gastrointestinal processing of many drugs but further data on their intestinal expression levels are required. Relative mRNA expression and relative and absolute protein expression data of transporters are commonly measured by real-time polymerase chain reaction (RT-PCR), Western blot and mass spectrometry-based targeted proteomics techniques. All of these methods, however, have their own strengths and limitations, and therefore, validation for optimized quantification methods is needed. As such, the identification of the most appropriate technique is necessary to effectively translate preclinical findings to first-in-human trials. In this study, the mRNA expression and protein levels of the efflux transporter P-glycoprotein (P-gp) in jejunal and ileal epithelia of 30 male and female human subjects, and the duodenal, jejunal, ileal and colonic tissues in 48 Wistar rats were quantified using RT-PCR, Western blot and liquid chromatography-tandem mass spectrometry (LC-MS/MS). A similar sex difference was observed in the expression of small intestinal P-gp in humans and Wistar rats where P-gp was higher in males than females with an increasing trend from the proximal to the distal parts in both species. A strong positive linear correlation was determined between the Western blot data and LC-MS/MS data in the small intestine of humans (R2 = 0.85). Conflicting results, however, were shown in rat small intestinal and colonic P-gp expression between the techniques (R2 = 0.29 and 0.05, respectively). In RT-PCR and Western blot, an internal reference protein is experimentally required; here, beta-actin was used which is innately variable along the intestinal tract. Quantification via LC-MS/MS can provide data on P-gp expression without the need for an internal reference protein and consequently, can give higher confidence on the expression levels of P-gp along the intestinal tract. Overall, these findings highlight similar trends between the species and suggest that the Wistar rat is an appropriate preclinical animal model to predict the oral drug absorption of P-gp substrates in the human small intestine.

M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines.

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) three-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. The FDM 3DP process begins with the production of drug-loaded filaments by hot melt extrusion (HME), followed by the printing of a drug product using a FDM 3D printer. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (http://m3diseen.com/predictions/).