IntiQuan provides Statistical, Mathematical, and Modeling & Simulation based consulting services.

We consistently deliver high quality, efficient, and impact-full analyses.

We work with companies of all sizes.

We work across all stages of drug-discovery and development, including support of post-marketing study design and analysis.

Transparency Commitment

IntiQuan does not only deliver slide decks or analysis reports.

We always deliver the complete analysis package, including all code, scripts, models, and related IntiQuan own software that were used to generate the communicated results.

This allows for complete transparency and builds trust with our clients.

Our team members are proficient in a wide variety of analysis techniques. Below is a quick overview over the most common analyses. All analyses apply to both pre-clinical and clinical data.

(Population) Pharmacokinetic Analyses

Informing the Dose / Concentration relationship

Gaining knowledge of inter-patient effects

  • Assessment of effect of patient properties on observed concentrations
    • Is the AUC higher in the Japanese vs. Chinese population?
    • Is there a difference between male and female patients?
    • Do comedications 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?
  • Are the absorption properties adequate?

We use compartmental PK modeling to support you with the characterization of the Dose / Concentration relationship of your compound. Allowing you to take the right decisions based on knowledge gained from your data.

A population PK analysis is typically a pre-requisite of a subsequent Dose / Exposure / Response characterization via a PK/PD analysis.

(Population) PK/PD Analyses

Informing the Dose / Exposure / Response relationship

Gain confidence for the decision of your 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 relationship?
  • How does the dose-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?

We use PK/PD modeling to support you with a robust characterization of the Dose / Exposure / Response relationships for safety and efficacy readouts. Allowing you to take the right decisions based on knowledge gained from your data.

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

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

Trial Simulations

Assess the likelihood of success of candidate study designs

Test 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?

We use previously established PK and PK/PD models to perform clinical trial simulations that are targeted to answer the critical questions that you have. Allowing 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.

Quantitative Systems Pharmacology (QSP)

Understand and quantify disease mechanisms and how your compounds impact them

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?

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.

Utility Analysis

Assess 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)

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 publically available information about the competitor(s).

Model-Based Meta-Analysis

Assess the performance of competitor compounds based on publically available data

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

  • 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?

Answers to the above questions will allow to map your compound in the competitive landscape.

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.

IntiQuan Tool Support (Tools)

As part of our commitment to transparency, we provide our modeling tools and related documentation material free of charge.

We support customers with services around all IntiQuan Tools, including

  • Deployment in the customers company.
  • A full unit-testing suite for IQM Tools is available on agreement, enabling seamless testing and validation of IQM Tools.
  • Customization (e.g., interface to a different desired data-standard).
  • Service agreement for IQM Tools, allowing customers to benefit from the latest (yet unpublished) versions and features, bug-fixes, general support.
  • As soon as the R-based IQR Tools are available the service offer is extended to these as well.

Workflow Development (Tools)

Typically,  in any scientific domain, some of the basic components of an analysis can be automated with work-flow based approaches, increasing efficiency, quality, and compliance.

Our freely provided tools are built in a very modular and hierarchical manner, allowing us to tailor custom solutions for most requirements in the Modeling and Simulation / Pharmacometric area.

You have recurring analyses that could be standardized?

Let us help you getting it done!

Examples for workflows that we have been implementing:

  • Population PK workflow (included in the freely available tools), see also here.
  • Simplified population PK workflow, suitable for DMPK groups who quickly want to evaluate a single ascending first-in-human PK study (included in the freely available tools).
  • Implemented for a client: workflow to analyze animal PKPD data to characterize the EC50. In this case the data comes in a reasonable standard form and multiple compounds are tested in the same animal disease model. Perfect opportunity for a workflow based approach.
  • Planned implementation for a client: workflow based approach to extrapolate animal data in standard format to human PK.

Training and Mentoring (Tools)

As part of our commitment to transparency, we provide our modeling tools and related documentation material free of charge.

As part of our paid services we offer tailored solutions for training and mentoring, related to our tools and more general Modeling and Simulation approaches.

  • IQM Tools presentations (in the future, once available, also for the R-based IQR Tools)
  • IQM Tools workshops, including hands-on exercises (in the future, once available, also for the R-based IQR Tools)
  • Training and mentoring of associates in the use of our tools
    • Remote or on client site
    • Using example data or clients real project data