Source code for pkpd.protocols.protocol_pkpd_dissolution_simulate

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# *
# * Authors:     Carlos Oscar Sorzano (info@kinestat.com)
# *
# * Kinestat Pharma
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# * This program is free software; you can redistribute it and/or modify
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import pyworkflow.protocol.params as params
from pyworkflow.protocol.constants import LEVEL_ADVANCED
from pkpd.models.dissolution_models import *
from pkpd.protocols.protocol_pkpd import ProtPKPD
from pkpd.objects import PKPDExperiment, PKPDSample, PKPDVariable, PKPDFitting, PKPDSampleFit
from pkpd.pkpd_units import createUnit, strUnit

# tested in test_workflow_dissolution.py

[docs]class ProtPKPDDissolutionSimulate(ProtPKPD): """ Simulate a dissolution profile\n Protocol created by http://www.kinestatpharma.com\n""" _label = 'simulate dissolution' # --------------------------- DEFINE param functions -------------------------------------------- def _defineParams(self, form): form.addSection('Input') form.addParam('allowTlag', params.BooleanParam, label="Allow lag", default=False, help='Allow lag time before starting dissolution (t-tlag)') form.addParam('modelType', params.EnumParam, choices=["Zero order", "First order", "Fractional", "Weibull", "Double Weibull", "Triple Weibull", "Higuchi", "Korsmeyer-Peppas", "Hixson-Crowell", "Hopfenberg", "Hill", "Makoid-Banakar", "Splines2", "Splines3", "Splines4", "Spline5", "Splines6", "Splines7", "Splines8", "Splines9", "Splines10"], label="Dissolution model", default=3, help='Zero order: Y=K*(t-[tlag])\n' \ 'First order: Y=Ymax*(1-exp(-beta*(t-[tlag])))\n' \ 'Fractional order: Y=Ymax-pow(Ymax^alpha-alpha*beta*t,1/alpha))\n' \ 'Weibull: Y=Ymax*(1-exp(-lambda*t^b))\n' \ 'Double Weibull: Y=Ymax*(F1*(1-exp(-lambda1*t^b1))+(1-F1)*(1-exp(-lambda2*(t-tlag2)^b2)))\n' \ 'Triple Weibull: Y=Ymax*(F1*(1-exp(-lambda1*t^b1))+F2*(1-exp(-lambda2*(t-tlag2)^b2))+(1-F1-F2)*(1-exp(-lambda3*(t-tlag3)^b3)))\n' \ 'Higuchi: Y=Ymax*t^0.5\n' \ 'Korsmeyer-Peppas: Y=Ymax*t^m\n' \ 'Hixson-Crowell: Y=Ymax*(1-(1-K*t)^3)\n' 'Hopfenberg: Y=Ymax*(1-(1-K*t)^m)\n' 'Hill: Y = Ymax*t^d/(g^d+t^d)\n' 'Makoid-Banakar: Ymax*(t/tmax)^b*exp(b*(1-t/tmax))\n' 'SplinesN: Y= Ymax*Bspline(t;N,tmax)\n') form.addParam('parameters', params.StringParam, label="Parameters", default="", help='Parameter values for the simulation.\nExample: 2;5 is 2 for the first parameter, 5 for the second parameter\n' 'Zero order: [tlag];K\n' 'First order: [tlag];Ymax;beta\n' 'Fractional order: [tlag]; Ymax;beta;alpha\n' 'Weibull: [tlag]; Ymax;lambda;b\n' 'Double Weibull: [tlag]; Ymax; lambda1; b1; F1; tlag2; lambda2; b2\n' 'Triple Weibull: [tlag]; Ymax; lambda1; b1; F1; tlag2; lambda2; b2; F2; tlag3; lambda3; b3\n' 'Higuchi: [tlag]; Ymax\n' 'Korsmeyer-Peppas: [tlag]; Ymax; m\n' 'Hixson-Crowell: [tlag]; Ymax; K\n' 'Hopfenberg: [tlag]; Ymax; K; m\n' 'Hill: [tlag]; Ymax; g; d\n' 'Makoid-Banakar: [tlag]; Ymax; Tmax; b\n' 'SplinesN: [tlag]; Ymax; tmax; c1; c2; ...; cN\n') form.addParam('timeUnits', params.EnumParam, choices=["min", "h"], label="Time units", default=0) form.addParam('resampleT', params.FloatParam, label='Simulation model time step=', default=1) form.addParam('resampleT0', params.FloatParam, label='Initial time for simulation', default=0) form.addParam('resampleTF', params.FloatParam, label='Final time for simulation', default=100) form.addParam('AUnits', params.StringParam, label="Dissolution units", default="%", help="%, mg/dL, ...") form.addParam('noiseType', params.EnumParam, label="Type of noise to add", choices=["None", "Additive", "Multiplicative"], default=0, expertLevel=LEVEL_ADVANCED, help='Additive: noise is normally distributed (mean=0 and standard deviation=sigma)\n' \ 'Multiplicative: noise is normally distributed (mean=0 and standard deviation=sigma*X)\n') form.addParam('noiseSigma', params.FloatParam, label="Noise sigma", default=0.0, expertLevel=LEVEL_ADVANCED, condition="noiseType>0", help='See help of Type of noise to add\n') # --------------------------- STEPS functions -------------------------------------------- def _insertAllSteps(self): self._insertFunctionStep('runSimulate') self._insertFunctionStep('createOutputStep')
[docs] def addNoise(self, y): if self.noiseType.get() == 0: return y elif self.noiseType.get() == 1: return y + np.random.normal(0.0, self.noiseSigma.get(), y.shape) elif self.noiseType.get() == 2: return y * (1 + np.random.normal(0.0, self.noiseSigma.get(), y.shape))
[docs] def runSimulate(self): tvar = PKPDVariable() tvar.varName = "t" tvar.varType = PKPDVariable.TYPE_NUMERIC tvar.role = PKPDVariable.ROLE_TIME if self.timeUnits.get() == 0: tvar.units = createUnit("min") elif self.timeUnits.get() == 1: tvar.units = createUnit("h") Avar = PKPDVariable() Avar.varName = "A" Avar.varType = PKPDVariable.TYPE_NUMERIC Avar.role = PKPDVariable.ROLE_MEASUREMENT if self.AUnits.get() != "%": Avar.units = createUnit(self.AUnits.get()) else: Avar.units = createUnit("none") self.experimentSimulated = PKPDExperiment() self.experimentSimulated.variables["t"] = tvar self.experimentSimulated.variables["A"] = Avar self.experimentSimulated.general["title"] = "Simulated dissolution profile" self.experimentSimulated.general["comment"] = "" if self.modelType.get() == 0: self.model = Dissolution0() elif self.modelType.get() == 1: self.model = Dissolution1() elif self.modelType.get() == 2: self.model = DissolutionAlpha() elif self.modelType.get() == 3: self.model = DissolutionWeibull() elif self.modelType.get() == 4: self.model = DissolutionDoubleWeibull() elif self.modelType.get() == 5: self.model = DissolutionTripleWeibull() elif self.modelType.get() == 6: self.model = DissolutionHiguchi() elif self.modelType.get() == 7: self.model = DissolutionKorsmeyer() elif self.modelType.get() == 8: self.model = DissolutionHixson() elif self.modelType.get() == 9: self.model = DissolutionHopfenberg() elif self.modelType.get() == 10: self.model = DissolutionHill() elif self.modelType.get() == 11: self.model = DissolutionMakoidBanakar() elif self.modelType.get() == 12: self.model = DissolutionSplines2() elif self.modelType.get() == 13: self.model = DissolutionSplines3() elif self.modelType.get() == 14: self.model = DissolutionSplines4() elif self.modelType.get() == 15: self.model = DissolutionSplines5() elif self.modelType.get() == 16: self.model = DissolutionSplines6() elif self.modelType.get() == 17: self.model = DissolutionSplines7() elif self.modelType.get() == 18: self.model = DissolutionSplines8() elif self.modelType.get() == 19: self.model = DissolutionSplines9() elif self.modelType.get() == 20: self.model = DissolutionSplines10() self.model.allowTlag = self.allowTlag.get() self.model.parameters = [float(x) for x in self.parameters.get().split(';')] newSample = PKPDSample() newSample.sampleName = "simulatedProfile" newSample.variableDictPtr = self.experimentSimulated.variables newSample.descriptors = {} newSample.addMeasurementPattern(["A"]) t0 = self.resampleT0.get() tF = self.resampleTF.get() deltaT = self.resampleT.get() t = np.arange(t0, tF + deltaT, deltaT) self.model.setExperiment(self.experimentSimulated) self.model.setXVar("t") self.model.setYVar("A") self.model.calculateParameterUnits(newSample) y = self.model.forwardModel(self.model.parameters, t) newSample.addMeasurementColumn("t", t) newSample.addMeasurementColumn("A", y[0]) self.experimentSimulated.samples[newSample.sampleName] = newSample fnExperiment = self._getPath("experimentSimulated.pkpd") self.experimentSimulated.write(fnExperiment) self.fittingSimulated = PKPDFitting() self.fittingSimulated.fnExperiment.set(fnExperiment) self.fittingSimulated.predictor=self.experimentSimulated.variables["t"] self.fittingSimulated.predicted=self.experimentSimulated.variables["A"] self.fittingSimulated.modelDescription=self.model.getDescription() self.fittingSimulated.modelParameters = self.model.getParameterNames() self.fittingSimulated.modelParameterUnits = self.model.parameterUnits sampleFit = PKPDSampleFit() sampleFit.sampleName = newSample.sampleName sampleFit.x = [t] sampleFit.y = y sampleFit.yp = y sampleFit.yl = y sampleFit.yu = y sampleFit.parameters = self.model.parameters sampleFit.modelEquation = self.model.getEquation() sampleFit.R2 = -1 sampleFit.R2adj = -1 sampleFit.AIC = -1 sampleFit.AICc = -1 sampleFit.BIC = -1 sampleFit.significance = ["NA" for prm in sampleFit.parameters] sampleFit.lowerBound = ["NA" for prm in sampleFit.parameters] sampleFit.upperBound = ["NA" for prm in sampleFit.parameters] self.fittingSimulated.sampleFits.append(sampleFit) fnFitting = self._getPath("fittingSimulated.pkpd") self.fittingSimulated.write(fnFitting)
[docs] def createOutputStep(self): self._defineOutputs(outputExperiment=self.experimentSimulated) self._defineOutputs(outputFitting=self.fittingSimulated)