Modular Hardware Interface for Nanoporous Membrane Filtration Experiments

Towards a Bioartificial Kidney: Validating Nanoporous Filtration MembranesJacob Bumpus, BME/EE 2014Casey Fitzgerald, BME 2014Michael Schultis, BME/EE 2014BackgroundIn 2010, 600,000 patients were treated for end stage renal disease (ESRD) in the US alone Treatment Options:Kidney transplantDonor ShortageDialysisCostly and time consuming

Concept illustration of an implantable bioartificial kidney. Courtesy of Shuvo RoyImage Citation:Fissell, William H., Shuvo Roy, and Andrew Davenport. "Achieving more frequent and longer dialysis for the majority: wearable dialysis and implantable artificial kidney devices."Kidney international84.2 (2013): 256-264.600,000 patients were treated for end stage renal disease (ESRD) in the US alone in 2010Current treatment procedures include kidney transplant and routine dialysisDialysis: COSTLY$$: ~$65,000/patient/yr. TIME: often requiring 3 treatments /wk. Significant shortage of donor organs for transplant means that many patients are left with no options other than years of routine dialysis Development of an implantable bioartificial kidney (BAK) would revolutionize treatment of end stage renal disease (ESRD).Improve patient outcomesReduce economic burden of treatmentBackgroundDr. Fissell is working to develop an implantable bioartificial kidney using nanoporous silicon membranes as biological filters

These chips feature nanometer-scale pore arrays, invisible to optical characterization methods

Screenshots courtesy UCSF School of Pharmacyhttp://pharmacy.ucsf.edu/kidney-project/

Problem StatementThese membranes must be thoroughly tested to verify their filtration characteristics

These experiments are manually monitored, and data is collected by hand

Current experiments are unable to simulate physiologically relevant fluid flow profiles, and are limited to constant flow rates

There are no failsafes to protect these expensive and fragile membranes

-In order to verify the silicon chips received from their collaborators, the Fissell Lab uses a set of experiments to measure the chips filtration performance under a variety of conditions and correlate this to their pore sizes-The Fissell lab must manually configure these filtration experiments, monitor them continuously throughout their duration (sometimes days to weeks long), and collect data by hand-Current experiments are unable to simulate physiologically relevant fluid flow profiles, and are limited to constant flow rates-No failsafes exist in order to protect the silicon membranes from being damaged in the event of deviations from preset conditions

Needs StatementTo design an integrated hardware/software suite that will streamline verification of nanoporous silicon filtration membranes while maximizing experimental control and minimizing user involvementGoalsDevelop an intuitive graphical user interface (GUI) that allows the user to easily control the system

Automate the experimental protocol and data collection

Allow user-defined hardware setup so that numerous experiments can be run simultaneously

Add programatic flow rate control to allow for pulsatility

Include failsafes and shutdown procedures to protect these membranes

-The experiments should be fully automated, permitting the lab technician to begin the experiments and then cease involvement except for occasional system monitoring-Allow user-defined hardware setup so that numerous different experiments can be run from the same system that is modular and expandable-An intuitive graphical user interface (GUI) should be developed in order to allow the user to control multiple experiments in an effective and efficient manner so that setting the experiment parameters is secondary to deciding what the parameters should be.-Add flow rate control and dialysate measurement to the current pressure control feedback system.

Clinical RelevanceOur design:Increases efficiency of experimentation by fully automating a variety of test protocols, allowing the group to characterize more chipsReduces project risk of lost time and money by adding failsafes against chip fracture ($1000s/chip)Maximizes experimental control by tightly coupling pressure monitoring to hardware output and adjusting for temporal driftAdds greater experimental relevance by allowing an adaptable physiological input platform, including simulation of pathophysiologic pressure conditions (hypertension)

ExperimentsThe solution must automate three modes of experimentationHydraulic Permeability ModeMeasures filtration rate as a function of pressure (uL/min/psi)

Filtration ModeCollect filtrate samples for further analysis

Dialysis ModeCollect samples in a closed blood/dialysate system

Filtration and Dialysis Mode should include an option to run with constant flow or a pulsatile waveformExperimental Setup Hydraulic PermeabilityPSIPeristaltic PumpWaterPressure TransducerAir RegulatorTo House AirAirFiltration MembraneImplantable DeviceZero0.0150.0100.005g0.0000.020Feedback Control DiagramArduino/LabVIEWPressure RegulatorPID Loop

Pressure TransducerConvert VISetpoint PressurePressureVoltageSignalADCVoltageErrorVPump VIPeristaltic PumpSetpoint Flow or WaveformRS-232SignalPressureHagen-PoiseuilleFlow RatePFiltration MembraneMass BalanceFiltration RateComparison VI (Actual > Setpoint?)SetpointMassSample MassYes/NoShutdown?V

Modified from Zhang, Guanqun, Jin-Oh Hahn, and Ramakrishna Mukkamala. "Tube-load model parameter estimation for monitoring arterial hemodynamics."Engineering Approaches to Study Cardiovascular Physiology: Modeling, Estimation, and Signal Processing(2011): 20.Pulsatility: Replicating Arterial Pressure Waveforms ex vivoControl Box ConceptPressure Transducers12436587General Purpose USB1234567141312111098Pressure Regulators12345678Power SupplyAC Power LineUSB Hubs and Female Connector Ports

HNG24125-12Through Hole BoardControl Box: Front ViewControl Box: Top ViewCRHydraulic PermeabilityFiltrationDialysisQuadrant 1Quadrant 2Quadrant 4Quadrant 3Top Level MenuSoftware Architecture DiagramExperiment OverviewExperimental Runtime GUIPressure TransducerTransducer Calibration

Mass BalanceExperimental Runtime GUIPeristaltic PumpSyringe Pump

InProgress

Fail SafesSet point = 0 Overrides the PID controllerRecord Max/Min PressureAlert user of potential errorsNext: Automatic shut-downError HandlingWhat to do if something goes wrong?

Error Handling Demo gif

Recent ProgressLabVIEW Control ofPressure transducer (COMPLETE)Pressure Regulator (COMPLETE)Peristaltic Pump (COMPLETE)Mass balance (COMPLETE)Syringe Pump (TBD) Initial iterations of pulsatile flowAbstract submission to American Society for Artificial Internal Organs (ASAIO) Student Design CompetitionFully Automated Hydraulic Permeability and Filtration ExperimentsPrimary Fail-safes and Error handlingParts have arrivedNext StepsRevisit pulsatility using known pressure profiles

Evaluate syringe pump functionality/feasibility

Compile individual components into a single, unified system

Order and assemble boxGantt Chart

Special Thanks To:Vanderbilt University Medical CenterVanderbilt School of EngineeringVanderbilt Renal Nanotechnology LabDr. William FissellJoey GroszekDr. Amanda BuckDr. Tim HolmanDr. A.B. BondsDr. Matthew Walker IIIJustMyPACE Peer Senior Design Group

Questions?Feedback Control DiagramArduino/LabVIEWPressure Regulator 1PID Loop

Pressure Regulator 2Pressure Transducer 2Pressure Transducer 1Conversion VISetpoint PressurePressure(Blood)Pressure(Dialysate)VoltageSignal 1ADCVoltageSignal 2ADCVVoltageErrorVoltageVoltagePump VIPeristaltic PumpsSetpoint Flows or WaveformsRS-232SignalsPressure(Blood)HPFlow RatePressure(Dialysate)PExperimental Setup Dialysis ModePSIPSITo House AirPeristaltic PumpPeristaltic PumpPressure TransducerDialysate SideBlood SidePressure TransducerAir RegulatorTo House AirAirAirFiltration MembraneSyringe PumpHydraulic Permeability Mode

Fissell, William H., et al. "High-performance silicon nanopore hemofiltration membranes."Journal of membrane science326.1 (2009): 58-63.Filtration/Dialysis Mode0Ideal FiltrationExample 1 psi PressureExample 2 psi PressureFiltrate Mass/ Original Mass ()Size (arbitrary units)Previous System

Previous Interface

Appendix: Feedback Control SimplifiedArduino/LabVIEWPressure Regulator 1PID Loop

Pressure Regulator 2Pressure Transducer 2Pressure Transducer 1Conversion VISetpoint PressurePressure(Dialysate)VoltageSignal 1ADCVoltageSignal 2ADCVVoltageErrorVoltageVoltagePressure(Blood)Appendix: Feedback Control DiagramArduino/LabVIEWPressure Regulator 1PID Loop

Pressure Regulator 2Pressure Transducer 2Pressure Transducer 1Conversion VISetpoint PressurePressure(Blood)Pressure(Dialysate)VoltageSignal 1ADCVoltageSignal 2ADCVVoltageErrorVoltageVoltagePump VIPeristaltic PumpSetpoint Flow or WaveformRS-232SignalFlow RatePressure(Blood)

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Hardware SelectDesign FactorsSoftware PlatformLabVIEW more $ / much less development time

Software concurrencyMore fewer programs running but internals are more complex

Hardware connectionsFewer cheaper in size and $ but more technically challenging