By: Paresh Chothe
New chemical entities (NCEs) are usually biotransformed into active and/or inactive chemical species, which is routinely demonstrated in preclinical animal models. But most often these animal models represent inevitable metabolic differences compared to humans leading to inaccurate anticipation of metabolic profile of NCE in humans.
Primary human hepatocyte (PHH) culture is considered to be the closest in vitro model to the human liver, and thus remains a gold standard in in vitro drug metabolism, pharmacokinetics (PK), and drug-drug interactions (DDI) studies in drug discovery. Both freshly isolated and cryopreserved human hepatocytes are successfully utilized in two dimensional format (plated or suspended) in drug metabolism studies to overcome the difficulty in extrapolating from animal data. Although the traditional PHH 2D culture is the most preferred in vitro screening and optimization tool for potential drug candidates in metabolic stability, clearance, and DDI prediction, it suffers from short ex-vivo life and rapid decline in gene expression of DMEs and drug transporters leading to underprediction of drug clearance and poor in vitro-in vivo extrapolation. In the past few years, a lot of interest has grown in developing novel 3D engineered liver models to overcome such limitations associated with the traditional 2D hepatic systems.
The micro-patterned hepatocyte co-culture models like HepatoPac and Hµrel are gaining wide recognition owing to their extended ex-vivo life span of up to 4 weeks. These novel models display higher and consistent expression of DMEs and drug transporters enabling them to capture more in vivo-like metabolic profile and clearance rate of slow clearance drugs. Additionally these systems have improved cellular microenvironment, in vivo-like polarity, correct localization of sinusoidal and canalicular transporters, and functional bile network, which is critical in understanding enzyme-transporter interplay.
In light of these 3D models, recent studies demonstrated that the HepatoPac model has robust functional expression of major uptake transporters (OATP1B1, 1B3, OCT1, and NTCP) compared to 2D monoculture, and it has great potential in CYP3A4-induction DDI risk prediction and CYP3A4 protein Kdeg assessment.
In the last decade, the sandwich-cultured hepatocyte (SCH) system has been extensively used particularly in mechanistic understanding of altered hepatic bile acid disposition by drugs and drug-induced liver injury (DILI). In addition to SCH, HepatoPac and Hµrel systems may provide an alternate approach in hepatobiliary drug disposition and DILI due to their extended life span and more stable functional cellular machinery. Moreover, these systems are amenable to high throughput unlike SCH.
In addition to these co-culture models, a few more 3D hepatocyte models such as liver-on-a-chip and Organovo, are getting increasing attention not only in drug metabolism but also in mechanistic understanding of liver diseases. A liver-on-chip is a dynamic 3D microfluidic liver model in which PHH are cultured with non-parenchymal cells (endothelial, stellate, and kufpper cells) under continuous perfusion to recapitulate in vivo-like architecture and cellular microenvironment and thereby achieve the level of normal liver functionality. This platform is believed to mimic the liver more at a physiological level with greater capabilities in DDI prediction, safety, and efficacy assessment of drug candidates. Organovo’s ExVive is a 3D bioprinted human liver tissue in which hepatocytes along with other cell types are architectured in a spatially controlled manner to precisely mimic in vivo liver tissue. It has a great potential in liver toxicity prediction.
While conventional hepatocyte 2D culture remains the workhorse in routine drug metabolism and pharmacokinetics (DMPK) and safety assessments, it is apparent that next generation liver models are superior for reasons of longer culture time and in vivo-like microenvironment and functionality, and hence they offer enormous potential in various aspects of drug discovery from biology, DMPK, safety/efficacy to toxicological investigations. Despite having tremendous capabilities, these novel platforms necessitate comprehensive evaluation to validate and foster their applications in drug metabolism and DDI risk prediction of potential drug candidates. Also the high cost and limited high throughput may be a concern for industry. Each model has advantages and disadvantages so careful evaluation might bring up the highest potential of each system in one or more areas in drug discovery in terms of better prediction of clinical outcome and improved in vitro-in vivo correlation that is being constantly demanded in drug development process.