Cardiovascular microphysiological systems (CVMPS) for safety studies – a pharma perspective
Bibliographic record
Abstract
The integrative responses of the cardiovascular (CV) system are essential for maintaining blood flow to provide oxygenation, nutrients, and waste removal for the entire body. Progress has been made in independently developing simple in vitro models of two primary components of the CV system, namely the heart (using induced pluripotent stem-cell derived cardiomyocytes) and the vasculature (using endothelial cells and smooth muscle cells). These two in vitro biomimics are often described as immature and simplistic, and typically lack the structural complexity of native tissues. Despite these limitations, they have proven useful for specific "fit for purpose" applications, including early safety screening. More complex in vitro models offer the tantalizing prospect of greater refinement in risk assessments. To this end, efforts to physically link cardiac and vascular components to mimic a true CV microphysiological system (CVMPS) are ongoing, with the goal of providing a more holistic and integrated CV response model. The challenges of building and implementing CVMPS in future pharmacological safety studies are many, and include a) the need for more complex (and hence mature) cell types and tissues, b) the need for more realistic vasculature (within and across co-modeled tissues), and c) the need to meaningfully couple these two components to allow for integrated CV responses. Initial success will likely come with simple, bioengineered tissue models coupled with fluidics intended to mirror a vascular component. While the development of more complex integrated CVMPS models that are capable of differentiating safe compounds and providing mechanistic evaluations of CV liabilities may be feasible, adoption by pharma will ultimately hinge on model efficiency, experimental reproducibility, and added value above current strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".