Source code for pkpd.protocols.protocol_pkpd_apply_allometric_scaling

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# * Authors:     Carlos Oscar Sorzano (
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# * Kinestat Pharma
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import math

import pyworkflow.protocol.params as params
from .protocol_pkpd import ProtPKPD
from pkpd.objects import PKPDExperiment, PKPDAllometricScale

[docs]class ProtPKPDApplyAllometricScaling(ProtPKPD): """ Apply an allometric scaling previously calculated to an incoming experiment. The labels specified by the allometric scaling model will be rescaled to the target weight. Note that depending on the exponent of the fitting you may want to use a different predictor (weight*maximum lifespan potential, or weight*brain weight) see the rule of exponents (Mahmood and Balian 1996). """ _label = 'apply allometric' #--------------------------- DEFINE param functions -------------------------------------------- def _defineParams(self, form): form.addSection('Input') form.addParam('inputPopulation', params.PointerParam, label="Input bootstrap population", pointerClass='PKPDFitting', help='The PK parameters of this experiment will be modified according to the allometric scale model.') form.addParam('inputAllometric', params.PointerParam, label="Allometric model", pointerClass='PKPDAllometricScale', help='All variables specified by the allometric scale model will be adjusted') form.addParam('targetWeight', params.FloatParam, label="Target weight (kg)", default=25, help='The PK parameters will be adjusted to this target weight') #--------------------------- INSERT steps functions -------------------------------------------- def _insertAllSteps(self): self._insertFunctionStep('runAdjust', self.targetWeight.get()) self._insertFunctionStep('createOutputStep') #--------------------------- STEPS functions --------------------------------------------
[docs] def runAdjust(self, targetWeight): scaleModel = PKPDAllometricScale() scaleModel.load(self.inputAllometric.get().fnScale.get()) self.population = self.readFitting(self.inputPopulation.get().fnFitting,cls="PKPDSampleFitBootstrap") self.experiment = PKPDExperiment() self.experiment.load(self.population.fnExperiment.get()) for sampleFit in self.population.sampleFits: sample = self.experiment.samples[sampleFit.sampleName] sampleWeight = float(sample.getDescriptorValue(scaleModel.predictor)) sample.setDescriptorValue(scaleModel.predictor,targetWeight) for varName, varUnits in scaleModel.averaged_vars: if varName in self.population.modelParameters: idx = self.population.modelParameters.index(varName) targetValue = scaleModel.models[varName][0] sampleFit.parameters[0][idx] = targetValue for varName, varUnits in scaleModel.scaled_vars: if varName in self.population.modelParameters: idx = self.population.modelParameters.index(varName) k = scaleModel.models[varName][0] a = scaleModel.models[varName][1] targetValue = k*math.pow(targetWeight,a) currentValue = k*math.pow(sampleWeight,a) for j in range(sampleFit.parameters.shape[0]): sampleFit.parameters[j][idx] *= targetValue/currentValue self.experiment.write(self._getPath("experiment.pkpd")) self.population.fnExperiment.set(self._getPath("experiment.pkpd")) self.population.write(self._getPath("bootstrapPopulation.pkpd"))
[docs] def createOutputStep(self): self._defineOutputs(outputExperiment=self.experiment) self._defineOutputs(outputPopulation=self.population) self._defineSourceRelation(self.inputPopulation, self.experiment) self._defineSourceRelation(self.inputAllometric, self.experiment) self._defineSourceRelation(self.inputPopulation, self.population) self._defineSourceRelation(self.inputAllometric, self.population)
#--------------------------- INFO functions -------------------------------------------- def _summary(self): msg = ["Target weight: %f"%self.targetWeight.get()] return msg def _citations(self): return ['Sharma2009','Mahmood1996']