Consulting

Full suite of customized
Pharmacometrics services
to help you succeed

WE CAN SUPPORT YOUR PROJECT
IN ANY PHASE OF DRUG DEVELOPMENT

  • Target validation
  • Understanding of the drug and disease mechanisms
  • Optimization of experiments and study design
  • First-in-human dose definition
  • Proof-of-concept study design
  • Defining the dose range of interest to test in Phase IIb
  • Go/no-go decisions
  • Regulatory submission / registration
  • Interactions with Health Authorities
  • Pediatric Investigation Plan
  • Special populations
  • Design of marketing studies
  • Increasing market value

What?

Ensuring adequate information content of datasets that are going to be used for analyses

How?

Whether the data is from pre-clinical, clinical studies, or literature, we can transform it into the format that is best suited for your analyses. We can prepare the analysis datasets ourselves or we can use the datasets in any format that you provide.

What?

NCA is often used to determine the degree of exposure following administration of a drug, such as AUC, and other PK parameters.

  • What is the degree of exposure following administration of a drug?
  • What is the clearance of a drug?
  • What is the half-life of a drug?
  • Are the pharmacokinetics dose-proportional?

How?

We use NCA to support you with the characterization of pharmacokinetic parameters.

What?

Gaining knowledge of inter-patient effects

  • Assessment of the effect of patient properties on observed concentrations
    • How does drug exposure compare in the Japanese vs. Chinese population?
    • Is there a difference between male and female patients?
    • Do co-medications influence the PK?
  • Animal to human PK extrapolation
    • Can allometric scaling be used?
    • Do we need more complex models, considering target concentrations in different tissues?
    • What are the expected human PK parameters?

How?

We use compartmental, nonlinear mixed effect modeling to support you with the characterization of the dose-concentration relationship of your compound. This allows you to take the right decisions based on knowledge gained from the data.

A population PK analysis is typically a prerequisite of a subsequent dose-exposure-response characterization via a population PK/PD or exposure-response analysis.

What?

Gaining confidence for the decision of the dosing strategy, based on quantification of safety and efficacy responses and their relationship to the exposure 

  • Do we have a good understanding of the minimal effective dose? 
  • Have we maxed out the response?
  • Is there a dose-response or exposure-response relationship?
  • How does the dose/exposure-response relationship depend on patient properties?
  • Is the observed inter-patient variability mainly driven by PK or PD or both?
  • Which dose range should we test in the next study/studies?

How?

We use population PK/PD modeling or exposure-response analysis to support you with a robust characterization of the dose-exposure-response relationships for safety and efficacy readouts. This allows you to take the right decisions based on knowledge gained from the data.

The model could include available knowledge about the mechanism of action, the underlying disease dynamics, and the patient properties.

A population PK/PD analysis is typically a prerequisite for a subsequent study assessment and optimization using Clinical Trial simulation.

What?

Generate First-in-Man dose and efficacy predictions in-silico by integration of in-vitro and/or in-vivo non-clinical and clinical experiments, and guide the design of your clinical trials.

  • How do drug properties scale to exposure and effect?
  • Do we understand the drug PK?
  • Do we understand the disease mechanism?
  • Do we understand how our compound acts on the disease?
  • Why are pre-clinical in-vivo outcomes not consistent?
  • Why do in-vitro results not match observed in-vivo outcomes?
  • How can the mechanism of action be translated into the clinic?
  • What are predictive biomarkers for the clinical outcome of interest?
  • What are beneficial drug-combinations for the given disease mechanics and available targets?
  • Which experiments (in-vitro and/or in-vivo) should we conduct to better inform the disease mechanism and/or drug action?
  • How can we leverage our clinical data to inform parameter values in QSP models?
  • What are the main biological mechanisms that drive the observations and allow translation into the clinic?
  • What are the most influential system parameters? Can we identify them based on available clinical data? What would need to be measured in clinical studies to be able to do so?

How?

Physiologically-based (PK and QSP) modelling leverages the wealth of data gathered throughout the drug discovery & development process. It is capable of integrating biological knowledge and prior data for building and simulating models that integrate across all biological scales. This unique capability allows to link experimental in-vitro model systems with observations in animal experiments and clinical trials. The relation of patients, diseases, and drugs but also more specialized topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, drug-drug-gene, or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

Our Partner has outstanding expertise in the development of mechanistic, physiologically-based (PB) simulation models that cover many different aspects of drug pharmacokinetics (PK) and pharmacodynamics (PD) including oral and subcutaneous administration, post-absorptive distribution, metabolization and elimination as well as drug effect, drug response and disease modeling. For their work, our Partner is using the R statistics computing software and the Open-Systems-Pharmacology Suite (OSPS, www.open-systems-pharmacology.org) with the tools PK-Sim® and MoBi®.

What?

Testing various trial designs in-silico before running the actual study to establish the design that has the highest likelihood of success.

  • What is the advantage of a more complex adaptive design?
  • How many patients do we need per group?
  • What dose levels should we test?
  • Which patient populations should be included to optimize the likelihood of success?
  • How do the expected results compare to what is known about the standard-of-care and/or competitor compounds?

How?

We use previously established population PK and PK/PD models to perform clinical trial simulations that are targeted to answer the critical questions that you have. This allows you to design a trial that maximizes the likelihood of success, based on knowledge gained from your data and models that have been built based on your data.

In order to compare the results of the clinical trial simulations with the expected performance of competitor compounds it is good to perform a Model-Based Meta-Analysis to assess all publically available information about the competitor(s) and compare it to the outcome of the simulations for your compound.

What?

Generate biological/pharmacological hypotheses in-silico to aid in the design of in-vitro or in-vivo non-clinical and clinical experiments, and guide biomedical experiments so that they yield more meaningful data.

  • Do we understand the disease mechanism?
  • Do we understand how our compound acts on the disease?
  • Why do in-vitro results not match observed in-vivo outcomes?
  • How can the mechanism of action be translated into the clinic?
  • What are predictive biomarkers for the clinical outcome of interest?
  • What are beneficial drug-combinations for the given disease mechanics and available targets?
  • Which experiments (in-vitro and/or in-vivo) should we conduct to better inform the disease mechanism and/or drug action?
  • How can we leverage our clinical data to inform parameter values in QSP models?
  • What are the main biological mechanisms that drive the observations and allow translation into the clinic?
  • What are the most influential system parameters? Can we identify them based on available clinical data? What would need to be measured in clinical studies to be able to do so?

How?

Quantitative Systems Pharmacology (QSP) can be seen as a sub-discipline of traditional Pharma Modeling and Simulation (Pharmacometrics). The main difference is that models tend to be more mathematically complex, which makes them unsuitable to handle and understand by the traditional approaches.

IntiQuan has strong experience and background in the area of complex biological and biomedical modeling and simulation approaches and can support you gaining the information you need about how your compound is working in the context of the targeted disease.

We have methodology and tools in place, which allow a seamless linking of even the most complex systems pharmacology models with your clinical data.

What?

Assessing the potential of your compound, considering safety, efficacy, and the competitive landscape

  • Do we have a wide enough therapeutic window?
  • Will we be competitive based on what we know about the competitor compounds?
  • How do we differentiate from the competition (speed of onset, maintenance effect level, side-effects, compliance, etc.)?
  • Are we at least better than the standard of care?
  • Should we continue the development of the compound?
  • Should we consider different dosing schedules or change absorption properties? (Impact of such modifications could be assessed using Clinical Trial Simulations)

How?

We use previously established PK, PK/PD, and possibly also more mechanistic models to assess the questions that you might have and which most likely are very similar to the ones listed above. Allowing you to confidently take decisions, based on knowledge gained from your data and models that have been built based on your data.

In order to compare your compound to competitor compounds it is good to have previously performed a Model-Based Meta-Analysis to assess all publicly available information about the competitor(s).

What?

Allowing you to evaluate your compound against the competition before investing in the next studies, based on information from the scientific literature, regulatory databases, and conference proceedings. Mapping your compound in the competitive landscape.

  • Which measures for the effect of a drug are most important for management of the disease?
  • Is data publically available that allows to assess competitor compunds on such measures?
    • Examples for measures: time to onset, maximum response, duration of response, responder rate, etc.
  • What are the typical effect levels for the competitors on these measures?
  • Are there known population effects related to disease or mechanism of action?

How?

Depending on the outcome of this mapping additional analyses could be performed to assess if your compound has potential against the competition. Clinical Trial Simulation could be used to evaluated alternative dosing schedules that can be targeted to beat the competition in certain measures. For example a certain loading regimen could be used to improve the speed-of-onset.

Model-Based Meta-Analysis can also be used to assess biomarkers as predictors for clinically meaningful benefit and allow incorporation of such information into a more comprehensive Dose / Concentration / Biomarker / Clinical Outcome model.

We can support you in assessing the potential of your compound in relationship to the competition and the standard-of-care.

LET'S EXPLORE HOW WE CAN BE HELPFUL