SCHLIEREN, Switzerland—InSphero AG has launched what it says is the industry’s first automation-compatible 3D human liver disease platform—known as the 3D InSight Human Liver Disease Discovery Platform—for for non-alcoholic steatohepatitis (NASH) and non-alcoholic fatty liver disease (NAFLD) drug discovery.
In what InSphero described as a breakthrough in 3D cell technology for drug discovery and safety testing, the platform has been precisely engineered to include all the human liver cell types and inducers necessary to replicate progression of NASH in patients, from fatty liver (steatosis) to inflammation (NASH) and scarring (fibrosis) of the liver. This preclinical discovery platform enables scalable in-vitro drug efficacy assessment, screening, combinatorial testing and the study of complex NASH pathophysiology.
“Development of novel therapeutics for NASH has been impeded by the lack of predictive in-vitro models that reflect the complex mechanisms underlying disease initiation and progression in patients,” explained InSphero CEO and co-founder Dr. Jan Lichtenberg. “With the 3D InSight Human Liver Disease Platform for NAFLD and NASH, we are filling a huge unmet need in the research community for fast, efficient screening of drug candidates.”
He added that mimicking human tissue or organs is much better than using rodent models, and comparing healthy tissue to diseased tissue is much better than looking at diseased tissue alone. Mouse models can take six months to produce results, he pointed out, while 3D InSight can perform the analysis in two to three weeks.
NASH, a severe type of fatty liver disease that can lead to cirrhosis or carcinoma, has a market that could reach $20 billion to $35 billion by 2025. Up to 40 percent of the U.S. population may suffer from a precursor disease called non-alcoholic fatty liver (NAFL), closely associated with type 2 diabetes. Up to 30 percent of these patients will go on to develop NASH.
There are no FDA-approved drugs for NASH, and Phase 3 trial results have yielded little. Compounds are failing or showing mixed results in which the impact is offset by potential liver toxicity. The problem is the lack of predictive in-vitro models that reflect the complex mechanisms underlying disease initiation and progression in patients.
Dr. Scott Friedman, dean for therapeutic discovery and chief of liver diseases at the Icahn School of Medicine at Mount Sinai, explained, “Modeling all the elements of this human disease has been extremely challenging. Drug screens with animal models of NASH can take months and do not always accurately reflect whether a drug will work on humans. InSphero’s platform offers a possible alternative for rapid screening of large numbers of drugs and combinations of drugs at different doses.”
InSphero is working with NASH drug developers to integrate this automation-compatible platform into their discovery workflows. The scalable Akura technology underlying the company’s 3D InSight Discovery and Safety Platforms includes 96 and 384-well plate formats and the Akura Flow organ-on-a-chip system to drive efficient innovation throughout all phases of drug development. Future releases will use the company’s Akura Flow organ-on-a-chip technology to combine liver disease and diabetes platforms into one system for the study of systemic metabolic disease. Eventually, InSphero hopes to cover more liver diseases, type 1 and 2 diabetes models, and the interconnection between the pancreas and the liver.
“We want to create more insight for customers to be able to see the interactions of drugs and tissue types,” Lichtenberg reported. “What we have observed is that switching from animal testing to in-vitro testing offers a more ethical and inexpensive alternative, but researchers considered it a mediocre alternative. Now we can offer uniformity and reproducibility, the analytical tools have evolved substantially and interest in human-only targets and novel modalities has grown. In-vitro tests are preferable to animal tests, because they are faster, more predictive and offer more options to achieve the desired goals.”
He concluded, “We’re getting an extremely positive reception, because people were feeling the lack of predictive, translatable models. It gives us a lot of energy to continue with our work.”
