Computer simulated modeling of healthy and diseased right ventricular and pulmonary circulation

Physiological Modeling of Hemodynamic Responses to Sodium Nitroprusside

BACKGROUND

Computational modeling of physiology has become a routine element in the development, evaluation, and safety testing of many types of medical devices. Members of the Food and Drug Administration have recently published a manuscript detailing the development, validation, and sensitivity testing of a computational model for blood volume, cardiac stroke volume, and blood pressure, noting that such a model might be useful in the development of closed-loop fluid administration systems. In the present study, we have expanded on this model to include the pharmacologic effect of sodium nitroprusside and calibrated the model against our previous experimental animal model data.

METHODS

Beginning with the model elements in the original publication, we added six new parameters to control the effect of sodium nitroprusside: two for the onset time and clearance rates, two for the stroke volume effect (which includes venodilation as a "hidden" element), and two for the direct effect on arterial blood pressure. Using this new model, we then calibrated the predictive performance against previously collected animal study data using nitroprusside infusions to simulate shock with the primary emphasis on MAP. Root-mean-squared error (RMSE) was calculated, and the performance was compared to the performance of the model in the original study.

RESULTS

RMSE of model-predicted MAP to actual MAP was lower than that reported in the original model, but higher for SV and CO. The individually fit models showed lower RMSE than using the population average values for parameters, suggesting the fitting process was effective in identifying improved parameters. Use of partially fit models after removal of the lowest variance population parameters showed a very minor decrement in improvement over the fully fit models.

CONCLUSION

The new model added the clinical effects of SNP and was successfully calibrated against experimental data with an RMSE of <10% for mean arterial pressure. Model-predicted MAP showed an error similar to that seen in the original base model when using fluid shifts, heart rate, and drug dose as model inputs.

In-silico analysis of closed-loop vasopressor control of phenylephrine versus norepinephrine

We have previously demonstrated in in-silico, pre-clinical animal models, and finally human clinical studies the ability of a novel closed-loop vasopressor titration system to manage norepinephrine infusion rates to keep mean arterial blood pressure in a very tight range, reduce hypotension time and severity, and reduce overtreatment. We hypothesized that the same controller could, with modification for pharmacologic differences, suitably titrate a lower-potency longer duration of action agent like phenylephrine. Using the same physiologic simulation model as was used previously for in-silico testing of our controller for norepinephrine, we first updated the model to include a new vasopressor agent modeled after phenylephrine. A series of simulation tests patterned after our previous norepinephrine study was then conducted, this time using phenylephrine for management, in order to both test the system with the new agent and allow for comparisons between the two. Hundreds of simulation trials were conducted across a range of patient and environmental variances. The controller performance was characterized based on time in target, time above and below target, coefficient of variation, and using Varvel's criteria. The controller kept the simulated patients' MAP in target for 94% of management time in the simple scenarios and more than 85% of time in the most challenging scenarios. Varvel criteria were all under 1% error for expected pharmacologic responses and were consistent with those established for norepinephrine in our previous studies. The controller was able to acceptably titrate phenylephrine in this simulated patient model consistent with performance previously seen for norepinephrine after adjusting for the anticipated differences between the two agents.

Semi-mechanistic modelling platform to assess cardiac contractility and haemodynamics in preclinical cardiovascular safety profiling of new molecular entities

BACKGROUND AND PURPOSE

Cardiovascular safety is one of the most frequent causes of safety-related attrition both preclinically and clinically. Preclinical cardiovascular safety is routinely assessed using dog telemetry monitoring key cardiovascular functions. The present research was to develop a semi-mechanistic modelling platform to simultaneously assess changes in contractility (dPdt_max ), heart rate (HR) and mean arterial pressure (MAP) in preclinical studies.

EXPERIMENTAL APPROACH

Data from dPdt_max , HR, preload (left ventricular end-diastolic pressure [LVEDP]) and MAP were available from dog telemetry studies after dosing with atenolol (n = 27), salbutamol (n = 5), L-N^G -nitroarginine methyl ester (L-NAME; n = 4), milrinone (n = 4), verapamil (n = 12), dofetilide (n = 8), flecainide (n = 4) and AZ001 (n = 14). Literature model for rat CV function was used for the structural population pharmacodynamic model development. LVEDP was evaluated as covariate to account for the effect of preload on dPdt_max .

KEY RESULTS

The model was able to describe drug-induced changes in dPdt_max , HR and MAP for all drugs included in the developed framework adequately, by incorporating appropriate drug effects on dPdt_max , HR and/or total peripheral resistance. Consistent with the Starling's law, incorporation of LVEDP as a covariate on dPdt_max to correct for the preload effect was found to be statistically significant.

CONCLUSIONS AND IMPLICATIONS

The contractility and haemodynamics semi-mechanistic modelling platform accounts for diurnal variation, drug-induced changes and inter-animal variation. It can be used to hypothesize and evaluate pharmacological effects and provide a holistic cardiovascular safety profile for new drugs.

Closed-loop vasopressor control: in-silico study of robustness against pharmacodynamic variability

Initial feasibility of a novel closed-loop controller created by our group for closed-loop control of vasopressor infusions has been previously described. In clinical practice, vasopressor potency may be affected by a variety of factors including other pharmacologic agents, organ dysfunction, and vasoplegic states. The purpose of this study was therefore to evaluate the effectiveness of our controller in the face of large variations in drug potency, where 'effective' was defined as convergence on target pressure over time. We hypothesized that the controller would remain effective in the face up to a tenfold variability in drug response. To perform the robustness study, our physiologic simulator was used to create randomized simulated septic patients. 250 simulated patients were managed by the closed-loop in each of 7 norepinephrine responsiveness conditions: 0.1 ×, 0.2 ×, 0.5 ×, 1 ×, 2 ×, 5 ×, and 10 × expected population response to drug dose. Controller performance was evaluated for each level of norepinephrine response using Varvel's criteria as well as time-out-of-target. Median performance error and median absolute performance error were less than 5% in all response levels. Wobble was below 3% and divergence remained negative (i.e. the controller tended to converge towards the target over time) in all norepinephrine response levels, but at the highest response level of 10 × the value approached zero, suggesting the controller may be approaching instability. Response levels of 0.1 × and 0.2 × exhibited significantly higher time-out-of-target in the lower ranges (p < 0.001) compared to the 1 × response level as the controller was slower to correct the initial hypotension. In this simulation study, the closed-loop vasopressor controller remained effective in simulated patients exhibiting 0.1 to 10 × the expected population drug response.