加载Target-Mediated药物Disposition (TMDD) Model。

sbioloadprojecttmdd_with_TO.sbproj

设置目标占用（TO) as a response.

cs = getconfigset(m1); cs.RuntimeOptions.StatesToLog ='至';

Get the dosing information.

d = getdose(m1,'每日剂量'）；

使用A扫描不同剂量的数量Simbiology.scenarios目的。为此，首先参数化数量剂量的财产。然后使用Scenarios目的。

量表= addParameter（M1，'AmountParam','单位'，D.Amountunits）；d.amount ='AmountParam'; d.Active = 1; doseSamples = SimBiology.Scenarios('AmountParam'，linspace（0,300,31））;

Create aSimFunction模拟模型。放TO作为仿真输出。

％抑制模拟过程中发出的信息警告。警告（'离开','simbiology：simfunction：doses_not_empty'）；f = createSimFunction(m1,doseSamples,'至'，d）

f = SimFunction Parameters: Name Value Type Units _______________ _____ _____________ ____________ {'AmountParam'} 1 {'parameter'} {'nanomole'} Observables: Name Type Units ______ _____________ _________________ {'TO'} {'parameter'} {'dimensionless'} Dosed: TargetName TargetDimension Amount AmountValue AmountUnits _______________ ___________________________________ _______________ ___________ ____________ {'Plasma.Drug'} {'Amount (e.g., mole or molecule)'} {'AmountParam'} 1 {'nanomole'} TimeUnits: day

警告（'on','simbiology：simfunction：doses_not_empty'）；

使用由Scenarios目的。In this case, the object generates 31 different doses; hence the model is simulated 31 times and generates aSimDataarray.

doseTable = getTable(d); sd = f(doseSamples,cs.StopTime,doseTable)

Simbiology仿真数据阵列：31-1 by-1模型名称：TMDD记录数据：物种：0隔室：0参数：1敏感性：0可观察：0

绘制仿真结果。还添加两条参考线，代表安全性和功效阈值TO。In this example, suppose that anyTOvalue above 0.85 is unsafe, and anyTOvalue below 0.15 has no efficacy.

h = sbioplot（SD）;时间= SD（1）.Time;H.NextPlot ='add'; safetyThreshold = plot(h,[min(time), max(time)],[0.85, 0.85],'DisplayName','Safety Threshold'）；efficacyThreshold = plot(h,[min(time), max(time)],[0.15, 0.15],'DisplayName',“功效阈值”）；

Postprocess the simulation results. Find out which dose amounts are effective, corresponding to theTOresponses within the safety and efficacy thresholds. To do so, add an observable expression to the simulation data.

％抑制模拟过程中发出的信息警告。警告（'离开','SimBiology:sbservices:SB_DIMANALYSISNOTDONE_MATLABFCN_UCON'）；新闻= addobservable(sd,'stat1','max(TO) < 0.85 & min(TO) > 0.15','单位',“无量纲”)

Simbiology仿真数据阵列：31-1 by-1模型名称：TMDD记录数据：物种：0隔室：0参数：1敏感性：0敏感性：0可观察：1

The addobservable function evaluates the new observable expression for eachSimData在sd并将评估结果作为新的结果返回SimDataarray,新闻, which now has the added observable (stat1）。

Simbiology将可观察结果存储在A的两个不同特性中SimData目的。If the results are scalar-valued, they are stored insimdata.scalarobservables。否则，它们被存储在SimData.VectorObservables。在此示例中，stat1可观察expression is scalar-valued.

Extract the scalar observable values and plot them against the dose amounts.

scalarObs = vertcat(newSD.ScalarObservables); doseAmounts = generate(doseSamples); figure plot(doseAmounts.AmountParam,scalarObs.stat1,'o','MarkerFaceColor','b')

该图显示，剂量范围从50到180纳莫尔提供TOresponses that lie within the target efficacy and safety thresholds.

You can update the observable expression with different threshold amounts. The function recalculates the expression and returns the results in a newSimData对象数组。

新闻2 = updateobservable(newSD,'stat1','最大（TO）<0.75＆min（to）> 0.30'）；

Rename the observable expression. The function renames the observable, updates any expressions that reference the renamed observable (if applicable), and returns the results in a newSimData对象数组。

newsd3 = RenameObservable（newsd2，'stat1',“效应”）；

恢复警告设置。

警告（'on','SimBiology:sbservices:SB_DIMANALYSISNOTDONE_MATLABFCN_UCON'）；