Source code for pkpd.protocols.protocol_pkpd_simulate_dose_escalation

# **************************************************************************
# *
# * Authors:     Carlos Oscar Sorzano (
# *
# * Kinestat Pharma
# *
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License as published by
# * the Free Software Foundation; either version 2 of the License, or
# * (at your option) any later version.
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * GNU General Public License for more details.
# *
# * You should have received a copy of the GNU General Public License
# * along with this program; if not, write to the Free Software
# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
# * 02111-1307  USA
# *
# *  All comments concerning this program package may be sent to the
# *  e-mail address ''
# *
# **************************************************************************

import pyworkflow.protocol.params as params
from .protocol_pkpd import ProtPKPD
from pkpd.objects import PKPDDoseResponse
from pkpd.utils import parseRange
from pkpd.models.pd_models import *
from numpy.random import uniform

[docs]class ProtPKPDSimulateDoseEscalation(ProtPKPD): """ Simulate a dose escalation\n Protocol created by\n""" _label = 'simulate dose escalation' #--------------------------- DEFINE param functions -------------------------------------------- def _defineParams(self, form, fullForm=True): form.addSection('Input') form.addParam('modelType', params.EnumParam, choices=["OQuigley0","OQuigley1", "OQuigley2", "Sigmoid", "Gompertz", "Logistic", "Richards"], label="Response model", default=0, help='OQuigley0: Y=((tanh(X)+1)/2)^a. Order: a\n'\ 'OQuigley1: Y=((tanh(X-X0)+1)/2)^a. Order: X0;a\n'\ 'OQuigley2: Y=exp(g*(X-X0))/(1+exp(g*(X-X0)). Order: X0;g\n'\ 'Sigmoid: Y=((X**h)/((X50**h)+(X**h))). Order X50;h\n'\ 'Gompertz: Y=exp(-exp(g*(X-X0))). Order: X0;g\n'\ 'Logistic: Y=1/(1+exp(g*(X-X0))). Order: X0;g\n'\ 'Richards: Y=1/((1+exp(g*(X-X0)))^(1/d)). Order: X0;g;d\n') form.addParam('paramValues', params.StringParam, label="Parameter values", default="", help='Parameter values for the simulation.\nExample: 3.5;-1 is 3.5 for the first parameter, -1 for the second parameter\n' 'OQuigley0: a\n'\ 'OQuigley1: X0;a\n'\ 'OQuigley2: X0;g\n'\ 'Sigmoid: X50;h\n'\ 'Gompertz: X0;g\n'\ 'Logistic: X0;g\n'\ 'Richards: X0;g;d\n') form.addParam('reportX', params.StringParam, label="Evaluate at X=", default="", help='Evaluate the model at these X values\nExample 1: [0,5,10,20,40,100]\nExample 2: 0:2:10, from 0 to 10 in steps of 2') form.addParam('doLog', params.BooleanParam, label="Take log10 in the dose", default=False, help='In the formulas, X is substituted by log10(X)') form.addParam('prot1ptr', params.PointerParam, label="Previous dose response", pointerClass='ProtPKPDDoseEscalation,ProtPKPDSimulateDoseEscalation',allowsNull=True, help='Optional. You may link this simulation to a previous dose escalation analysis, so that you may know the sequence') #--------------------------- INSERT steps functions -------------------------------------------- def _insertAllSteps(self): self._insertFunctionStep('runSimulate', self.modelType.get(), self.paramValues.get(), self.reportX.get()) #--------------------------- STEPS functions --------------------------------------------
[docs] def runSimulate(self, modelType, paramValues, reportX): reportXorig = parseRange(self.reportX.get()) reportX = parseRange(self.reportX.get()) if self.doLog: reportX=np.log10(np.clip(reportX,0.0,None)) print("Taking logarithms: X is really log10(X)") # Setup model self.printSection("Model setup") if self.modelType.get()==0: model = PDOQuigley0() elif self.modelType.get()==1: model = PDOQuigley1() elif self.modelType.get()==2: model = PDOQuigley2() elif self.modelType.get()==3: model = PDSigmoid() elif self.modelType.get()==4: model = PDGompertz() elif self.modelType.get()==5: model = PDLogistic1() elif self.modelType.get()==6: model = PDRichards() # Create list of parameters tokens=self.paramValues.get().split(';') if len(tokens)!=model.getNumberOfParameters(): raise Exception("The list of parameter values has not the same number of parameters as the model") model.parameters=[] for token in tokens: try: model.parameters.append(float(token.strip())) except: raise Exception("Cannot convert %s to float"%token) print("Simulated model: %s"%model.getEquation()) if reportX!=None: dr = PKPDDoseResponse() if self.prot1ptr.get() is not None:"dose_response.txt"), True) print("Evaluation of the model at specified values") yReportX = model.forwardModel(model.parameters, [reportX]) yReportX = yReportX[0] # From [array(...)] to array(...) print("==========================================") print("X Xused Ypredicted RandomResponse") print("==========================================") for n in range(0,reportX.shape[0]): response=uniform()<yReportX[n] dr.appendResponse(reportXorig[n],response) print("%f %f %f %s"%(reportXorig[n],reportX[n],yReportX[n],response)) print(' ') dr.write(self._getPath("dose_response.txt"))
#--------------------------- INFO functions -------------------------------------------- def _summary(self): msg=[] modelTypeStr="unknown" if self.modelType.get()==0: modelTypeStr = "OQuigley 0" elif self.modelType.get()==1: modelTypeStr = "OQuigley 1" elif self.modelType.get()==2: modelTypeStr = "OQuigley 2" elif self.modelType.get()==3: modelTypeStr = "Sigmoid" elif self.modelType.get()==4: modelTypeStr = "Gompertz" elif self.modelType.get()==5: modelTypeStr == "Logistic" elif self.modelType.get()==6: modelTypeStr == "Richards" msg.append("Model type: %s"%modelTypeStr) msg.append("Parameter values: %s"%self.paramValues) return msg