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Does anyone have a simple working example of code that uses simbiology to estimate parameters for a simple nonlinear pharamacodynamic model? I would like to first simulate data using a simple PKPD model, then estimate model parameters from the data. The code below creates the PK model. However, I have had no success trying to create the PD part. In the code below, the estimation is based on observing the drug concentrations over time. What I'd like to do instead is to pass the concentration values through a Hill function, i.e. if x is the concentration, then I want the observations to be y = R*x^g/(x^g+c^g) where R, g, and c are constants. I would like to allow the estimation routine to "see" only these nonlinearly transformed y values, and to estimate both the PK values (as is done in the code below), and also the parameters of the Hill equation, namely R, g, and c. Can anyone show me how to do this? Thanks in advance! Brandon ------------------------------------ %********************************* % *** simulate the data ********** %********************************* m1=sbiomodel('onecomp') r1=addreaction(m1,'Drug_Central -> null') % elimination k1 = addkineticlaw(r1,'MassAction') k1val = lognrnd(0, 0.2 , 1,1); p1 = addparameter(k1,'ke','Value',k1val,'ValueUnits','1/hour') k1.ParameterVariableNames='ke' % cannot seem to get the following part to work % r2=addreaction(m1,'Drug_Central -> x') % nonlinear observation % k2 = addkineticlaw(r2, 'Hill-Kinetics'); % k2.KineticLaw = 'Unknown'; % r2.ReactionRate = 'Rmax*x^gamma / ( x^gamma+A^gamma)'; % p2 = addparameter(k2, 'Rmax', 2.3); % p3 = addparameter(k2,'A',5); % p4 = addparameter(k2,'gamma',3); % set(p4, 'ValueUnits', 'dimensionless'); %% info for each constant rate interval % start time, end time, rate [figure out intermediate: amount] RateInfo=[2 4 10; 6 12 50; 12 20 90; 22 24 150; 25 27 200; 30 31 50]; d=[]; for i=1:size(RateInfo,1); dt = sbiodose('dt'); dt.TargetName = 'Drug_Central'; dt.RateUnits = 'milligram/hour'; dt.AmountUnits='milligram'; dt.TimeUnits = 'hour'; dt.Rate = RateInfo(i,3); t0=RateInfo(i,1); t1=RateInfo(i,2); amount=(t1-t0)*RateInfo(i,3); dt.Amount = amount; dt.StartTime=t0 dt.Active = true; d=[d dt]; end dose=d; %% run simulation cs = getconfigset(m1) cs.StopTime=48 cs.TimeUnits='hour' [t,sd,species]=sbiosimulate(m1,d) plot(t,sd); legend(species); xlabel('Hours'); ylabel('Drug Concentration'); % throw out some data to simulate sparse sampling in an experiment t=t(1:5:end); sd=sd(1:5:end); data=table(t,sd); % convert to table data.Properties.VariableNames{'t'}='Time'; data.Properties.VariableNames{'sd'}='Conc'; %% convert to groupedData object -- required for fitting with sbiofit gData=groupedData(data) gData.Properties.VariableUnits={'hour','milligram/liter'} gData.Properties %********************************* % *** estimate model from data *** %********************************* pkmd = PKModelDesign pkc1 = addCompartment(pkmd,'Central') pkc1.DosingType = 'Infusion' pkc1.EliminationType='linear-clearance' pkc1.HasResponseVariable=true [model,map]=construct(pkmd) configset = getconfigset(model) configset.CompileOptions.UnitConversion=true configset.SolverOptions.AbsoluteTolerance=1e-9 configset.SolverOptions.RelativeTolerance=1e-5 dose=d; % look at map to see variables responseMap = {'Drug_Central = Conc'}; paramsToEstimate = {'log(Central)','log(Cl_Central)'} estimatedParams = estimatedInfo(paramsToEstimate,'InitialValue',[1 1]) paramsToEstimate = {'log(Central)','log(Cl_Central)'}; estimatedParams = estimatedInfo(paramsToEstimate,'InitialValue',[1 1]); %% estimate the parameters fitConst = sbiofit(model,gData,responseMap,estimatedParams,dose) %% plot results fitConst.ParameterEstimates figure(1); plot(fitConst); %% compare estimate to truth disp('****************') fitConst.ParameterEstimates.Estimate(2) k1val

I have a multi-compartmental PK model that I am trying to parameterise utilising two different data-sets. One data-set is a short term data set (0 -> 24hrs) and the second data set is a single data point at 168 hrs. The only difference between the two is that the initial bolus dosage is a different amount (and so I cannot combine the two sets) How can I get SimBiology to run a parameter estimation where the output parameters are based off of both data sets? I am running MATLAB R2016b on Mac & Version 5.5 of SimBiology

SimBiology model describing the metabolism and distribution of TCE using a human PBPK model

Examples used in the March 2012 webinar: Using SimBiology for Mechanism-Based PK/PD Modeling

I can't seem to find the answer to my question in SimBiology's documentation. Does anyone know how Matlab calculates the non-compartmental AUC? Does it use the linear / log-linear trapezoidal rule? How many points does it use to extrapolate the curve to infinity? Does it use log or linear extension? Many thanks!