Multiple Myeloma Cell Drug Responses Differ in Thermoplastic vs PDMS Microfluidic Devices
Bibliographic record
Abstract
Poly(dimethylsiloxane) (PDMS) is a commonly used elastomer for fabricating microfluidic devices, but it has previously been shown to absorb hydrophobic molecules. Although this has been demonstrated for molecules such as estrogen and Nile Red, the absorption of small hydrophobic molecules in PDMS specifically used to treat cancer and its subsequent impact on cytotoxicity measurements and assays have not been investigated. This is critical for the development of microfluidic chemosensitivity and resistance assay (CSRA) platforms that have shown potential to help guide clinical therapy selection and which rely on the accuracy of the readout involving interactions between patient-derived cells and cancer drugs. It is thus important to address the issue of drug absorption into device material. We investigated drug absorption into microfluidic devices by treating multiple myeloma (MM) tumor cells with two MM drugs (bortezomib (BTZ) and carfilzomib (CFZ)) in devices fabricated using three different materials (polystyrene (PS), cyclo-olefin polymer (COP), and PDMS). Half-maximal inhibitory concentrations (IC 50 ) were obtained for each drug–material combination, and an increase in IC 50 of ∼4.3× was observed in PDMS devices compared to both thermoplastic devices. Additionally, each MM drug was exposed to polymer samples, and samples were analyzed using time-of-flight secondary ion mass spectrometry (ToF-SIMS) to characterize adsorption and absorption of the drugs into each material. ToF-SIMS data showed the bias observed in IC 50 values found in PDMS devices was directly related to the absorption of drug during dose–response experiments. Specifically, BTZ and CFZ absorption in both PS and COP were all in the range of ∼100–300 nm, whereas BTZ and CFZ absorption in PDMS was ∼5.0 and ∼3.5 μm, respectively. These results highlight the biases that exist in PDMS devices and the importance of material selection in microfluidic device design, especially in applications involving drug cytotoxicity and hydrophobic molecules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".