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The Evolution of Model-informed Precision Dosing through Co-developed Companion Tools

In a recent Clinical Pharmacology in Drug Development article, I examined the co-development of companion model-informed precision dosing (MIPD) tools during drug development. This would accelerate the generation of evidence needed to hasten the broader implementation of MIPD in the clinic.1  MIPD is a modeling and simulation (M&S) approach in healthcare that is used to predict the most effective and/or least toxic drug dose for a patient. MIPD could revolutionize healthcare by reducing the incidence of adverse drug reactions (ADRs), improving drug efficacy, and increasing patient adherence. Companion MIPD tools, based on physiologically based pharmacokinetic (PBPK) modeling or other modeling approaches, could potentially evolve into “dynamic” or “interactive” prescribing information (PI) that could be used to guide better dose selection for individual patients. This would be especially beneficial for the dosing of drugs with a narrow therapeutic index where a small change in dose can impact the drug’s therapeutic effect or risk of an adverse reaction for complex patients, e.g. neonates, infants or pediatric patients, patients with significant renal or hepatic impairment, geriatric patients, those with rare diseases, pregnant women, oncology patients, or those on polypharmacy who are at risk of drug-drug interactions (DDIs).

Using Virtual PK Studies to Guide Dose Selection

Currently, MIPD is limited to a small number of academic-hospital centers with strong pharmacology expertise, and its implementation remains poorly translated to wider clinical practice. However, model-informed drug development (MIDD) methods are now recognized by sponsors and regulators as essential to advancing drug development. A mechanistic in vitro-in vivo extrapolation (IVIVE)-linked PBPK approach provides a unique and flexible method to predict an initial new drug dose in patients by using prior knowledge of other drugs that have similar physiochemical and PK/PD characteristics to the drug in development. Integration of IVIVE-linked PBPK with a drug file, a population file, and the study design allows virtual PK studies to be conducted with various permutations of these system parameters. This can then guide dosing in a population of interest while diminishing the need for costly and/or ethically and logistically difficult studies while still informing the PI. The Virtual Twin™ Technology could be used in healthcare to predict the drug dose for an individual patient that is most likely to improve efficacy and/or lower the risk of toxicity. This approach has been shown to accurately predict olanzapine (OLZ) exposure in individual patients and as a method to predict which patients being treated for metastatic melanoma with mutated isoforms of the BRAF gene V600E and V600K are more likely to experience drug toxicity.

Strategies for Developing a PBPK Companion MIPD Tool

Early adoption prior to the start of a clinical trial and late adoption post drug-registration are two possible pathways for co-developing MIPD companion tools.

Early Adoption Pathway to Generate Evidence Required for Drug Registrations

The early adoption of MIPD strategy in drug development could allow initial model verification against a relatively uncomplicated system – the healthy volunteer. A virtual twin database of healthy volunteers in the phase 1 unit could be used to inform the M&S program of the sponsor. Due to the pathological changes and complexity of patients which makes PBPK M&S more challenging as the clinical program progresses, individualization of the system parameters that define the patient population is required to predict exposure with the goal of ultimately predicting drug response.

Late Adoption Pathway in Phase 4: Two Possible Strategies

*Requires retrospective verification based on the completed model prior to “real-world” testing

The two late adoption scenario(s) shown above would be tested in a prospective post-marketing Phase 4 clinical trial that compares MIPD versus traditional dosing. Funding of the studies can be shared between the sponsor and the key stakeholder(s) likely to benefit most from lower drug costs attributed to improved dosing. The late adoption MIPD strategy would be dependent on access to the patient information and biological samples necessary to retrospectively build virtual twins of clinical trial participants. Paramount to the success of this approach is having suitable validated virtual twin population files in various disease states.

Advantages of Co-developing Companion MIPD Tools

Implementing MIPD offers several potential advantages to industry including, but not limited to the following:

  1. Increasing the chances of clinical trial success
  2. Enabling the development of problem drugs where traditional approaches have failed
  3. Preparing industry for changes to how medications are remunerated so that if value-based pricing based on improving individual patient outcomes becomes a reality, the industry is ready
  4. Accelerating PBPK best practices for model development, refinement, verification, and clinical development
  5. Improved PI quality where a dynamic PI allows superior assessment of the risk: benefit of drug therapy and selection of better doses for patients

Successful Implementation of MIPD Strategies into Clinical Practice

The widespread implementation of MIPD into clinical practice is hindered by multiple barriers. However, many of these hurdles can be addressed with on-going collaborative efforts to improve PBPK science. Actions to overcome these barriers and accelerate MIPD into clinical practice include:

  • engaging clinicians as partners in implementing MIPD
  • developing intuitive tools for non-modelers
  • training clinicians in quantitative pharmacology
  • harmonizing and standardizing pharmacokinetic data collection, analysis, and reporting
  • improving communication by modelers to healthcare sponsors, providers, approvers, payers, and patient groups on the values of MIPD
  • co-developing companion MIPD tools


  1. Polasek,T.M., Rayner, C.R., Peck, R.W., Rowland, A, Kimko, H., & Rostami-Hodjegan, A. (2018) Toward Dynamic Prescribing Information: Codevelopment of Companion Model-Informed Precision Dosing Tools in Drug Development. Clinical Pharmacology in Drug Development. 8(4), 418-425.

To learn more about how model-based approaches can improve precision dosing in clinical care, watch my webinar.

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By: Thomas Polasek