Pre-Clinical Lead Optimization

An engineering approach to drug candidates

Fractal helps you incorporate best practices into a highly efficient and methodical approach to engineering the properties of your drug candidates. We help you to optimize the parameters that will help you obtain the greatest therapeutic index for your drug. We offer expertise in:
PK assessment and optimization
PK/PD guided optimization
Potency and affinity optimization
Assay and model system evaluation
Key questions:
Is the screening strategy translatable?
What is the optimal binding affinity?
What drug parameters do we focus on optimizing?
Can we widen the projected therapeutic index?
Learn more:
How to design an efficient screening strategy
Get your small molecule through lead optimization faster using model-based approaches
Model-based approaches for antibody-drug conjugate lead optimization
Model-based approaches to support efficient reformulation: a case study
Oncolytic viruses: avoiding pitfalls in lead selection using model-based approaches

Translational Modeling

Rational design of first-in-man trials

Using powerful modeling paradigms and Bayesian approaches, Fractal projects clinical pharmacokinetics, toxicology, and therapeutic index using pre-clinical data. We offer data discovery to identify critical gaps and create customized roadmaps to generate lean, cost-effective pre-clinical packages.
Understanding clinical performance: Data-driven models provide a translatable understanding of clinical potential.
Key questions:
What is the right dose and schedule?
Will the drug have a therapeutic window?
How do we design and interpret clinical PK and PD assays?
How do we select doses for toxicology studies?
What is the shortest path to clinical Poc?
Learn more:
How to translate activity when you don't have a validated efficacy model
Projecting the benefit of reformulation using translational modeling
Comparing your RNAi drug to the competition using translational modeling: a case study
Designing faster first-in-human clinical trials using Bayesian statistics

Combination Development

Finding the combinations which maximize therapeutic index

Fractal uses a data-driven mathematical framework to rigorously evaluate the likelihood of success for different possible drug combinations. We examine synergies in efficacy as well as overlapping toxicity profiles to identify the combinations with the widest therapeutic index in the clinic.
Sample dose-pair analysis: A sample isobologram analysis examining efficacy and toxicity to determine the combination with the best therapeutic index.
Key questions:
Will the combination show an added benefit?
What is the optimal dose of each of the two drugs?
Does schedule matter for this combination?
What drugs combine well with the drug of interest?
Learn more:
Combination drug development- there is a better way
Dose optimization for anticancer drug combinations: maximizing therapeutic index via clinical exposure toxicity/ preclinical exposure-efficacy modeling (published in Clinical Cancer Research)
Translational exposure-efficacy modeling to optimize the dose and schedule of taxanes combined with the investigational Aurora A kinase inhibitor MLN8237 (alisertib) (published in Mol. Cancer Ther.)

Patient Selection

Identifying the right population for the drug

Fractal approaches patient selection with a focus on translatability, characterizing directly translatable pre-clinical models and/or PK/PD relationships using mechanistically relevant biomarkers. Our approach includes:
Leveraging translational exposure-response modeling: Combining carefully designed studies to uncover exposureresponse relationships (in translatable pre-clinical models) with exposure projections in humans, we uncover the patient subpopulations most likely to respond to therapy.
Design of indication-seeking Phase II trials: Using either pre clinical exposure-response or clinical PK/PD relationships in a Bayesian statistical framework, we can design a more efficient clinical path for indication-seeking or patient selection, resulting in parsimonious trials with statistically rigorous inferences.
Key questions:
Is there a patient subpopulation that is more likely to respond to the drug?
What is the most cost-effective way to run PDX studies?
Can pre-clinical in vivo or ex vivo data be leveraged to make the Phase II trial more efficient?
Learn more:
Using patient-derived xenograft (PDX) models for patient selection: best practices
Designing clinical trials for retrospective patient selection: measure twice, cut once
Precision medicine by modeling pharmacokinetic and biomarker drivers of tumor kinetics: assessing effects of alisertib exposure and target snp status on antitumor activity. (Poster: American Society for Clinical Pharmacology and Therapeutics Annual Meeting, 2017)

Quantitative Systems Models

Optimizing parameters for the best therapeutic index

Fractal leverages quantitative systems modeling to explore "what-if" questions around how different parameters influence drug performance. Using our proprietary modeling platforms, we can compare the behavior of different molecules and help identify and optimize the most influential parameters. This takes the guesswork out of design and uses quantitative rationales to inform clinical choices and maximize outcomes.
A rational approach to optimization: Using a combined understanding of PK, tumor properties and drug characteristics, we create an analytical framework to predict efficacy.
Key questions:
How should the drug be optimized?
What parameters affect efficacy and toxicity?
What target density is necessary for efficacy?
How does drug binding impact potency?
What is the effect of target expression and turnover?
Learn more:
Identifying a Target Product Profile for bispecifics using quantitative systems pharmacology
A systems pharmacology approach to optimizing antibody-drug conjugates
Model-based optimization of RNAi therapeutics: a case study

Clinical Pharmacology

A quicker, cleaner path to Proof of Concept

Fractal provides modeling support for early-clinical programs up to Proof of Concept. Model-based techniques (such as disease progress models and Bayesian approaches for dose escalation) are powerful tools that allow for an efficient yet informative Phase 1/11 investigation. We specialize in designing and applying cutting-edge techniques to provide a rigorous basis for clinical development choices and stage-gate decisions.
Key questions:
At what dose levels should we expect to see toxicity?
What is the most efficient dose-escalation scheme?
Will inter-patient variability in PK (or PD) be an issue?
Is there a scientifically rigorous way to glean early signals of efficacy?
Learn more:
Skipping straight to the clinic: best practices for indications lacking validated preclinical efficacy models
Modeling tumor growth in animals and humans: an evolutionary approach (book chapter)
Directional inconsistency between response evaluation criteria in solid tumor (RECIST) time to progression and response speed and depth (published in Eur. J. Cancer)
Redeveloping to broaden the therapeutic window: the case study of Mylotarg
Population pharmacokinetics of immunity after COVID-19 infection and vaccination (preprint)
Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection (published in Antibody Therapeutics)

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