DDMODEL00000122: Samtani_2010_Hb1Ac_prediction

  public model
Short description:
Linear regression model to simulate the HbA1c value at the steady state from fasting plasma glucose (FPG)
PharmML 0.8.x (0.8.1)
  • Simple pharmacometric tools for oral anti-diabetic drug development: competitive landscape for oral non-insulin therapies in type 2 diabetes.
  • Samtani MN
  • Biopharmaceutics & drug disposition, 3/2010, Volume 31, pages: 162-177
  • Clinical Pharmacology-Advanced PK/PD Modeling and Simulation, Johnson & Johnson Pharmaceutical Research & Development, Raritan, New Jersey 08869, USA. msamtani@its.jnj.com
  • The objectives were to develop a translational model that will help select doses for Phase-3 trials based on abbreviated Phase-2 trials and understand the competitive landscape for oral anti-diabetics based on efficacy, tolerability and ability to slow disease progression. Data for eight anti-diabetics with temporal profiles for fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) from 12 publications were digitized. The monotherapy data consisted of FPG and HbA1c profiles that were modeled using a transit compartment model. Evaluation of the competitive landscape utilized HbA1c literature data for 11 drugs. For the safety metric, tolerability issues with anti-diabetic drug classes were tabulated. For disease progression, the coefficient of failure method was used for assessing data from two long-term trials. The transit rate constants were remarkably consistent across 12 trials and represent system-specific/drug-independent parameters. The competitive landscape analysis showed that the primary efficacy metric fell into two groups of DeltaHbA1c: >0.8% or approximately 0.8%. On the safety endpoints, older agents showed some tolerability issues while the new agents were relatively safe. Among the different drug classes, only the thiazolidinediones appeared to slow disease progression but may also increase heart failure risk. In conclusion, the ability of system-specific parameters to predict HbA1c provides a tool to predict the expected efficacy profile from abbreviated dose-finding trials. To be commercially viable, new drugs should improve DeltaHbA1c by about 0.8% or more and possess safety profiles similar to newer anti-diabetic agents. Thus, this study proposes a suite of simple yet powerful tools to guide type-2-diabetes drug development.
Paolo Magni
Context of model development: Diagnostic model; Clinical end-point;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: The objectives were to develop a translational model that will help select doses for Phase-3 trials based on abbreviated Phase-2 trials and understand the competitive landscape for oral anti-diabetics based on efficacy, tolerability and ability to slow disease progression. Data for eight anti-diabetics with temporal profiles for fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) from 12 publications were digitized. The monotherapy data consisted of FPG and HbA1c profiles that were modeled using a transit compartment model. Evaluation of the competitive landscape utilized HbA1c literature data for 11 drugs. For the safety metric, tolerability issues with anti-diabetic drug classes were tabulated. For disease progression, the coefficient of failure method was used for assessing data from two long-term trials. The transit rate constants were remarkably consistent across 12 trials and represent system-specific/drug-independent parameters. The competitive landscape analysis showed that the primary efficacy metric fell into two groups of ?HbA1c: >0.8% or ?0.8%. On the safety endpoints, older agents showed some tolerability issues while the new agents were relatively safe. Among the different drug classes, only the thiazolidinediones appeared to slow disease progression but may also increase heart failure risk. In conclusion, the ability of system-specific parameters to predict HbA1c provides a tool to predict the expected efficacy profile from abbreviated dose-finding trials. To be commercially viable, new drugs should improve ?HbA1c by about 0.8% or more and possess safety profiles similar to newer anti-diabetic agents. Thus, this study proposes a suite of simple yet powerful tools to guide type-2-diabetes drug development. ;
Modelling task in scope: simulation;
Nature of research: Clinical research & Therapeutic use;
Therapeutic/disease area: Endocrinology;
Annotations are correct.
This model is not certified.
  • Model owner: Paolo Magni
  • Submitted: Dec 12, 2015 3:23:51 PM
  • Last Modified: Oct 10, 2016 8:36:07 PM
Revisions
  • Version: 7 public model Download this version
    • Submitted on: Oct 10, 2016 8:36:07 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 5 public model Download this version
    • Submitted on: Jun 2, 2016 8:14:41 PM
    • Submitted by: Paolo Magni
    • With comment: Updated model annotations.
  • Version: 2 public model Download this version
    • Submitted on: Dec 12, 2015 3:23:51 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.

Name

Generated from MDL. MOG ID: Method_1_Samtani_mog

Independent Variables

T

Function Definitions

additiveError:real⁡additive:real=additive

Covariate Model: cm

Continuous Covariates

FPG

Parameter Model: pm

Random Variables

EPS_1vm_err.DV~Normal2⁡mean=0var=1

Population Parameters

BETA0
BETA1
RES

Structural Model: sm

Variables

HBA1C=pm.BETA0+pm.BETA1⋅cm.FPG

Observation Model: om1

Continuous Observation

Y=sm.HBA1C+additiveError⁡additive=pm.RES+pm.EPS_1

External Dataset

OID
nm_ds
Tool Format
NONMEM

File Specification

Format
csv
Delimiter
comma
File Location
Simulated_Samtani_2010_ss_data.csv

Column Definitions

Column ID Position Column Type Value Type
ID
1
id
int
TIME
2
idv
real
DV
3
dv
real
FPG
4
covariate
real
EV
5
undefined
real

Column Mappings

Column Ref Modelling Mapping
TIME
 T
DV
 om1.Y
FPG
 cm.FPG

Estimation Step

OID
estimStep_1
Dataset Reference
nm_ds

Parameters To Estimate

Parameter Initial Value Fixed? Limits
pm.BETA0
2.84
false
pm.BETA1
0.5
false
pm.RES
0
true

Operations

Operation: 1

Op Type
generic
Operation Properties
Name Value
algo
foce

Step Dependencies

Step OID Preceding Steps
estimStep_1
 
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