Pharmacodynamic modeling of sequence-dependent antitumor activity of insulin-like growth factor blockade and

Amit Khatri1, Richard C Brundage, Jessica M Hull

  • 1Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, 55455, USA.

The AAPS Journal
|November 22, 2011
PubMed

Insights

Administering Gemcitabine before the small molecule receptor tyrosine kinase inhibitor PQIP demonstrated superior in vitro antitumor effects compared to the reverse sequence. This finding is crucial for optimizing combination cancer therapy strategies.

Area of Science:

  • Pharmacology and Oncology
  • Drug Combinations and Sequencing

Background:

  • Insulin-like growth factor (IGF) signaling inhibitors are in clinical trials for cancer treatment.
  • Combination therapy may enhance antitumor effects, but optimal administration sequences are not well-defined.

Purpose of the Study:

  • To investigate the impact of administration sequence on the in vitro antitumor activity of Gemcitabine and a novel small molecule receptor tyrosine kinase inhibitor (PQIP).
  • To compare the efficacy of a forward (Gemcitabine then PQIP) versus a reverse (PQIP then Gemcitabine) treatment sequence.

Main Methods:

  • Three human breast cancer cell lines (MCF-7, MDA-MB-231, Hs-578T) were treated with Gemcitabine and PQIP as single agents and in combination.
  • Combination treatments were administered in both forward and reverse sequences.
  • Antitumor effects were assessed longitudinally using Bayesian analysis with WinBUGS for pharmacodynamic modeling.

Main Results:

  • The pharmacodynamic model accurately predicted observed cell-kill data.
  • Significant differences in cell-kill rate constants were observed between the forward and reverse sequences, ranging from 0.11 to 0.64 day(-1).
  • The superiority of one sequence over the other was generally dependent on the specific cell line and PQIP concentration used.

Conclusions:

  • The in vitro data strongly suggest that initiating treatment with Gemcitabine followed by PQIP is a more effective sequence than the reverse order.
  • These findings provide critical insights for optimizing the clinical application of this combination therapy in breast cancer.

Related Concept Videos

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...