Many polydimethylsiloxane (PDMS)-based devices, e . g ., organ-on-chip or microphysiological systems, have been developed to investigate biological processes at a miniaturized scale. These devices typically culture cells under microfluidic perfusion to dynamically dose cells with chemicals of interest; however, PDMS is known to interact with hydrophobic compounds and can strongly limit such compounds’ in-device bioavailability. Here, we quantify chemical-PDMS interactions for three commonly used steroid hormones: aldosterone, estradiol, and progesterone. We find that aldosterone does not detectably interact with PDMS; estradiol interacts modestly; and progesterone interacts strongly. Based on these measured interactions, we computationally model dynamic dosing protocols based on pulsed/bolus delivery and circadian control. We show that interactions with PDMS can strongly disrupt these dynamic dosing protocols in a chemical-specific and flow-rate-dependent manner. These results have critical implications for the use of steroid hormones in PDMS-based devices. ### Competing Interest Statement The authors have declared no competing interest.

An early study on the biological consequences of using polydimethysiloxane (PDMS) for microfluidic cell culture reported that estrogens could be sequestered by PDMS; however, the PDMS interaction parameters of specific hormones have not been reported. Without these parameters, it is not possible to assess whether such sequestration is a problem for a particular device and flow rate combination. Here we quantify chemical-PDMS interactions for a commonly used estrogen and two additional steroid-class hormones: estradiol, aldosterone, and progesterone. We find that aldosterone does not detectably interact with PDMS; estradiol interacts modestly; and progesterone interacts strongly. Based on these measured interactions, we computationally model dynamic dosing protocols based on pulsed/bolus delivery and circadian control. We show that interactions with PDMS can strongly disrupt these dynamic dosing protocols in a chemical-specific and flow-rate-dependent manner. Notably, estradiol-PDMS interactions can have significant impacts under static conditions or low flow rates, but those impacts become negligible at higher flow rates. These results have critical implications for the use of steroid hormones in PDMS-based microfluidic devices.

Organ-on-chip (OOC) devices are an emerging New Approach Method in both pharmacology and toxicology. Such devices use heterotypic combinations of human cells in a micro-fabricated device to mimic in vivo conditions and better predict organ-specific toxicological responses in humans. One drawback of these devices is that they are often made from polydimethylsiloxane (PDMS), a polymer known to interact with hydrophobic chemicals. Due to this interaction, the actual dose experienced by cells inside OOC devices can differ strongly from the nominal dose. To account for these effects, we have developed a comprehensive model to characterize chemical-PDMS interactions, including partitioning into and diffusion through PDMS. We use these methods to characterize PDMS interactions for 24 chemicals, ranging from fluorescent dyes to persistent organic pollutants to organophosphate pesticides. We further show that these methods return physical interaction parameters that can be used to accurately predict time-dependent doses under continuous-flow conditions, as would be present in an OOC device. These results demonstrate the validity of the methods and model across geometries and flow rates.

Polydimethylsiloxane (PDMS) is a glassy polymer widely used in biomedical engineering, namely in microfluidics applications. However, PDMS is known to interact with hydrophobic chemicals. This interaction is exacerbated at the scale of microfluidics, making careful modeling of in-device concentrations vital for PDMS-based microfluidic devices. While it has been previously reported that many chemicals diffuse through PDMS, here we report that diffusion in PDMS is anomalous, i.e. characterized by nonlinear, subdiffusive mean-squared displacements (MSD). We show that this anomalous diffusion can be modeled in the framework of stretched-time fractional diffusion, and report the transport parameters for a set of fluorescent tracer dyes. Depending on the device geometry and protocol, this anomalous behavior may have a significant impact, specifically in regards to cross-talk between microfluidic channels.

There is a need for valves and pumps that operate at the microscale with precision and accuracy, are versatile in their application, and are easily fabricated. To that end, we developed a new rotary planar multiport valve to faithfully select solutions (contamination = 5.22 ± 0.06 ppb) and a rotary planar peristaltic pump to precisely control fluid delivery (flow rate = 2.4 ± 1.7 to 890 ± 77 μL/min). Both the valve and pump were implemented in a planar format amenable to single-layer soft lithographic fabrication. These planar microfluidics were actuated by a rotary motor controlled remotely by custom software. Together, these two devices constitute an innovative microformulator that was used to prepare precise, high-fidelity mixtures of up to five solutions (deviation from prescribed mixture = ±|0.02 ± 0.02| %). This system weighed less than a kilogram, occupied around 500 cm3, and generated pressures of 255 ± 47 kPa. This microformulator was then combined with an electrochemical sensor creating a microclinical analyzer (μCA) for detecting glutamate in real time. Using the chamber of the μCA as an in-line bioreactor, we compared glutamate homeostasis in human astrocytes differentiated from human-induced pluripotent stem cells (hiPSCs) from a control subject (CC-3) and a Tuberous Sclerosis Complex (TSC) patient carrying a pathogenic TSC2 mutation. When challenged with glutamate, TSC astrocytes took up less glutamate than control cells. These data validate the analytical power of the μCA and the utility of the microformulator by leveraging it to assess disease-related alterations in cellular homeostasis.

Abstract In vivo tumor microenvironments are heterogeneous in their architecture, cellular contents, metabolic landscape, and extracellular matrix (ECM) composition. Moreover, these microenvironments can undergo temporal and spatial changes. This may alter tumor responses to anticancer therapies. To examine the complex and dynamic changes within tumors and in tumor microenvironments, we developed a computational single cell-based model of the three-dimensional multicellular organotypic cultures: Organoid3D. This model was calibrated to experimental data and reproduced growth dynamics and morphologies of four human breast cell lines: a nontumorigenic epithelial MCF-10A, a mildly tumorigenic MCF-10AT1, and metastatic MCF-10CA-1a and MCF-10CA-1d lines grown in various microenvironmental conditions. By explicitly including the fibril structure of the ECM in the model, we examined how ECM properties promote or suppress the growth of in silico tumor organoids. The quantitative integration of experimental data for organoids exposed to different concentrations of doxorubicin allowed us to derive hypotheses on relative importance of microenvironmental factors and chemotherapeutic treatments on organoid growth. Thus, the in silico organoid model interrogated with experimental data provides a tool for fast and broad hypotheses testing and presents an opportunity to explore experimental conditions beyond what is physically feasible in laboratory experiments. Citation Format: Katarzyna A. Rejniak, Sharan Poonja, Jessica Kingsley, Shreya Mathur, Ibrahim Chamseddine, Aleksandra Karolak, Dmitry A. Markov, Lisa J. McCawley. In silico organoids: A model for deconvolution of microenvironmental and drug effects on tumor growth [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr A47.

Microfluidic organ-on-chip devices constructed from polydimethylsiloxane (PDMS) have proven useful in studying both beneficial and adverse effects of drugs, supplements, and potential toxicants. Despite multiple advantages, one clear drawback of PDMS-based devices is binding of hydrophobic chemicals to their exposed surfaces. Chemical binding to PDMS can change the timing and extent of chemical delivery to cells in such devices, potentially altering dose-response curves. Recent efforts have quantified PDMS binding for selected chemicals. Here, we test a wider set of nineteen chemicals using UV-vis or infrared spectroscopy to characterize loss of chemical from solution in two setups with different PDMS-surface-to-solution-volume ratios. We find discernible PDMS binding for eight chemicals and show that PDMS binding is strongest for chemicals with a high octanol-water partition coefficient (log P > 1.85) and low H-bond donor number. Further, by measuring depletion and return of chemical from solution over tens to hundreds of hours and fitting these results to a first order model of binding kinetics, we characterize partitioning into PDMS in terms of binding capacities per unit surface area and both forward and reverse rate constants. These fitted parameters were used to model the impact of PDMS binding on chemical transport and bioavailability under realistic flow conditions and device geometry. The models predict that PDMS binding could alter in-device cellular exposures for both continuous and bolus dosing schemes by up to an order of magnitude compared to nominal input doses.

Background: Fluvoxamine therapy is used for treatment of patients with depressive disorder, but it is often ineffective, and some patients suffer from dose-dependent undesirable side effects such as vertigo, headache, indigestion, xerostomia, increased anxiety, etc. CYP2D6 is involved in the biotransformation of fluvoxamine. Meanwhile, the genes encoding these isoenzymes have a high level of polymorphism, which may affect the protein synthesis. Objective: The primary objective of our study was to investigate the effects of CYP2D6 genetic polymorphisms on the efficacy and safety of fluvoxamine in patients with depressive disorder and comorbid alcohol use disorder, in order to develop the algorithms of optimization of fluvoxamine therapy for reducing the risk of dose-dependent undesirable side effects and pharmacoresistance. Methods: The study involved 45 male patients (average age: 36.44 +/- 9.96 years) with depressive disorder and comorbid alcohol use disorder. A series of psychometric scales was used in the research. Genotyping of CYP2D6 (1846G>A) was performed using real-time polymerase chain reaction. Results: According to results of Mann-Whitney U-test, statistically significant differences between the efficacy and safety of fluvoxamine were obtained on 9th and 16th days of therapy in patients with GG and GA genotypes (The Hamilton Rating Scale for Depression: 10.0 [10.0; 23.0] vs 25.0 [24.0; 16.0] (P<0.001) on the 9th day and 4.0 [2.0; 5.0] vs 6.0 [6.0; 7.0] on the 16th day; The UKU Side Effect Rating Scale: 6.0 [4.0; 6.0] vs 9.0 [9.0; 10.0] (P<0.001) on the 9th day and 5.0 [1.0; 9.0] vs 19.0 [18.0; 22.0] on the 16th day). Conclusion: This study demonstrated the lower efficacy and safety of fluvoxamine in patients with depressive disorder and comorbid alcohol use disorders with GA genotype in CYP2D6 1846G>A polymorphic marker.

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

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BACKGROUND:Haloperidol is used for the treatment of alcohol use disorders in patients with signs of alcohol-related psychosis. Haloperidol therapy poses a high risk of adverse drug reactions (ADR). Contradictory data, which include the effects of genetic polymorphisms in genes encoding the elements of haloperidol biotransformation system on haloperidol metabolism rate and plasma drug concentration ratio, are described in patients with different genotypes. The primary objective of this study was to investigate the effects of CYP2D6 and CYP3A5 genetic polymorphisms on haloperidol equilibrium concentration in patients with alcohol use disorder.METHODS:The study included 69 male patients with alcohol use disorder. Genotyping was performed using the allele-specific real-time PCR. CYP2D6 and CYP3A were phenotyped with HPLC-MS using the concentration of endogenous substrate of the enzyme and its urinary metabolites [6-hydroxy-1,2,3,4-tetrahydro-β-carboline(6-HO-THBC) to pinoline ratio for CYP2D6 and 6-β-hydroxycortisol to cortisol ratio for CYP3A]. The equilibrium plasma concentration was determined using LC-MS-MS.RESULTS:Results indicated that both C/D indexes and equilibrium concentration levels depend on CYP2D6 genetic polymorphism, but only in patients receiving haloperidol intramuscular injections [0.26 (0.09; 0.48) vs. 0.54 (0.44; 0.74), p=0.037].CONCLUSIONS:The study demonstrates that CYP2D6 genetic polymorphism (1846G>A) can affect haloperidol concentration levels in patients with alcohol use disorder.

Tumor development and its response to therapies are highly affected by heterogeneous microenvironments, including fluctuations in chemical and physical cues, as well as in the composition of the surrounding extracellular matrix. For example, the abnormal topology of tumor vasculature and tumor tissue leads to disruption in oxygen transport and in the emergence of regions of low oxygen (hypoxic conditions), and in accumulation of acidic products and decreased pH (acidic conditions). In addition, the elevated mechanical tension is commonly observed in the tumor connective tissue (high tension conditions). All these factors affect tumor growth and efficacy of anticancer drugs. Moreover, the microenvironmental conditions can dynamically evolve after treatment that is difficult to investigate in vivo in a systematic way. To represent the complexity and dynamics of tumor microenvironment we developed a computational model for the growth of 3D organotypic cultures integrated with experimental organ-on-the-chip 3D cultures. The computational model was validated based on the data collected from cultured breast spheroids, including the non-tumorigenic epithelial MCF-10A cell line, mildly tumorigenic c-Ha-ras oncogene transfected MCF-10AT1 cell line, and a metastatic line MCF-10CA-1a. Based on the differences in these cell lines genetic profiles, cell mechanical properties, and image-guided variations in organotypic morphologies, the computational model was able to reproduce in a quantitative way the dynamics of 3D tumor spheroids’ growth under various microenvironmental conditions. This calibrated model was subsequently applied to predict tumor growth and its response to chemotherapy in heterogeneous microenvironments consisting of dynamically changing gradients of oxygen and pH, as well as tension of the surrounding microenvironment. The overarching aim of our project is to use this integrated (in silico-organ and organ-on-the-chip) approach to target the treatment of tumor 3D organotypic cultures with drugs that display different mechanism of action, such as cell cycle arrest, initiation of apoptosis, or treatment combinations. Furthermore, this approach will allow us to propose new treatment schedules and dosages that will be tested in the organ-on-the-chip experiments. The progress on this integrated model development will be presented.

The next generation of chemical toxicity testing will use organs-on-chips (OoCs)—3D cultures of heterotypic cells with appropriate extracellular matrices to better approximate the in vivo cellular microenvironment. Researchers are already working to validate whether OoCs are predictive of toxicity in humans. Here, we review two other key aspects of how OoCs may advance predictive toxicology—each taking advantage of OoCs as systems of intermediate complexity that remain experimentally accessible. First, the intermediate complexity of OoCs will help elucidate the scale(s) of organismal complexity that currently confound computational predictions of in vivo toxicity from in vitro data sets. Identifying the strongest confounding factors will help researchers improve the computational models underlying such predictions. Second, the experimental accessibility of OoCs will allow researchers to analyze chemical-exposure responses in OoCs using an array of high-content readouts—from fluorescent biosensors that report dynamic changes in specific cell signaling pathways to unbiased searches over broader biochemical space using technologies like ion mobility-mass spectrometry. Such high-content information on OoC responses will help determine the details of adverse outcome pathways. We note these possible uses of OoCs so that researchers and engineers can consider them in the design of next-generation OoC control, perfusion, and analysis platforms.

BACKGROUND:Understanding blood-brain barrier responses to inflammatory stimulation (such as lipopolysaccharide mimicking a systemic infection or a cytokine cocktail that could be the result of local or systemic inflammation) is essential to understanding the effect of inflammatory stimulation on the brain. It is through the filter of the blood-brain barrier that the brain responds to outside influences, and the blood-brain barrier is a critical point of failure in neuroinflammation. It is important to note that this interaction is not a static response, but one that evolves over time. While current models have provided invaluable information regarding the interaction between cytokine stimulation, the blood-brain barrier, and the brain, these approaches-whether in vivo or in vitro-have often been only snapshots of this complex web of interactions. METHODS:We utilize new advances in microfluidics, organs-on-chips, and metabolomics to examine the complex relationship of inflammation and its effects on blood-brain barrier function ex vivo and the metabolic consequences of these responses and repair mechanisms. In this study, we pair a novel dual-chamber, organ-on-chip microfluidic device, the NeuroVascular Unit, with small-volume cytokine detection and mass spectrometry analysis to investigate how the blood-brain barrier responds to two different but overlapping drivers of neuroinflammation, lipopolysaccharide and a cytokine cocktail of IL-1β, TNF-α, and MCP1,2. RESULTS:In this study, we show that (1) during initial exposure to lipopolysaccharide, the blood-brain barrier is compromised as expected, with increased diffusion and reduced presence of tight junctions, but that over time, the barrier is capable of at least partial recovery; (2) a cytokine cocktail also contributes to a loss of barrier function; (3) from this time-dependent cytokine activation, metabolic signature profiles can be obtained for both the brain and vascular sides of the blood-brain barrier model; and (4) collectively, we can use metabolite analysis to identify critical pathways in inflammatory response. CONCLUSIONS:Taken together, these findings present new data that allow us to study the initial effects of inflammatory stimulation on blood-brain barrier disruption, cytokine activation, and metabolic pathway changes that drive the response and recovery of the barrier during continued inflammatory exposure.

The blood-brain barrier (BBB) is a critical structure that serves as the gatekeeper between the central nervous system and the rest of the body. It is the responsibility of the BBB to facilitate the entry of required nutrients into the brain and to exclude potentially harmful compounds; however, this complex structure has remained difficult to model faithfully in vitro. Accurate in vitro models are necessary for understanding how the BBB forms and functions, as well as for evaluating drug and toxin penetration across the barrier. Many previous models have failed to support all the cell types involved in the BBB formation and/or lacked the flow-created shear forces needed for mature tight junction formation. To address these issues and to help establish a more faithful in vitro model of the BBB, we have designed and fabricated a microfluidic device that is comprised of both a vascular chamber and a brain chamber separated by a porous membrane. This design allows for cell-to-cell communication between endothelial cells, astrocytes, and pericytes and independent perfusion of both compartments separated by the membrane. This NeuroVascular Unit (NVU) represents approximately one-millionth of the human brain, and hence, has sufficient cell mass to support a breadth of analytical measurements. The NVU has been validated with both fluorescein isothiocyanate (FITC)-dextran diffusion and transendothelial electrical resistance. The NVU has enabled in vitro modeling of the BBB using all human cell types and sampling effluent from both sides of the barrier.