Cell to Cell Signaling
Cell to Cell Signaling
The SciFlow system allows for the culturing of the same cell types, or different cell types, within an inter-connected network of wells. Effectively, this causes downstream cells to be exposed to the biological response of the upstream cells. Being a platform technology, it is a simple SOP change to include divergent cell lines or distinct primary cells within the system. A user may fill multiple wells with same cell type to create a concentration gradient of the parent chemical and metabolites across the row (each well and each row generates unique concentration gradient over the course of the experiment). Alternatively, multiple different cell types may be placed in adjacent wells, mimicking the path of the chemical through the human biological system (i.e. intestine to liver to target organ of choice).
On average a pharmaceutical company will begin with over 10,000 compounds entering the drug discovery pipeline, progress 5 or fewer drug candidates into clinical trials, which will optimistically result in 1 approved treatment. This is a very long process, which averages from hundreds of millions to over a billion dollars per approved treatment. The majority of early testing is performed using in vitro models. Traditional static tissue culture plates are not ideal models for in vivo human drug exposure. Static models do not accurately replicate numerous in vivo processes, such as: dynamic in vivo plasma drug concentrations, metabolite effects, or inter-cellular communication. Therefore, current physiological outcomes of toxicity, safety, and efficacy are not realistically determined until compounds are evaluated in clinical trials. There is a compelling need for an in vitro technology that provides accurate predictions of toxicity and pharmacology. This technology needs to be physiologically relevant, grounded in human biology, incorporate flow conditions relevant to the in vivo environment, provide low (sub toxic) to high gradient exposures, and distinguish metabolism impacts from downstream bioactivations, in order to be pertinent to native in vivo systems.
One of the major reasons drugs fail in clinical trials is unexpected toxicity due to metabolism that was not accurately predicted in animal model systems or current cell culture models. Extensive efforts are used during lead optimization to avoid liver toxicity in particular and to accurately predict toxic bioactivation products that may be produced over time during the course of drug treatment. Currently the methods to determine whether a parent molecule produces a toxic (or bioactive) metabolite involves a combination of in silico modeling combined with technically challenging in vitro trapping or LC-MS studies of radiolabeled parent drugs followed by large scale HPLC purification and testing. In other words, a significant effort to both identify the metabolites isolating them is a prerequisite to determining their effects.
A highly predictive cell-based assay (e.g. SciFlow technology) could be used to categorize leads for bioactive toxic metabolites without a priori knowledge of the chemical structures. Successful implementation would therefore provide significant cost savings compared to current methods.
Critically, the SciFlow system allows the user to generate in vivo like non-linear exposure models in vitro. Non-linear concentration gradients are critical descriptors in many in vivo processes. For example, SciFlow has the potential to recapitulate the pharmacokinetics of an injected or ingested drug in vivo where the concentration of a drug is constantly changing due to principles of absorption, distribution, metabolism, and excretion (ADME). This attribute of the SciFlow system could vastly improve in vitro to in vivo extrapolation (IVIVE) affording better prediction of ultimate clinical outcomes. Enhanced predictivity and more relevant IVIVE conclusions are precisely the data sets desired by numerous industries.