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London Abstract

INFORMED DECISIONS FOR ALL.

An informed decision is made

based on all relevant data at time they matter

TRANSLATIONAL MODELLING

 

Compelling experimental evidence

TURNING ALL ACCESSIBLE DATA INTO COMPELLING EVIDENCE

 

With industrial-leading expertise in translational modelling, we help you unambiguously answer these critical questions to make timely, informed decisions throughout drug discovery and development to excel in today's tough competitions:

  • How do I forecast tumour growth inhibition (TGI) based on IC50 (in vitro potency)? 

  • Do my data confirm the (novel) therapeutic concept in animal studies to verify the proof of mechanism (PoM)? 

  • What xenograft TGI is needed to excel in direct and indirect competitions?

  • What is the target profile (e.g. potency, PK profile of a small molecule or biologics) I need to achieve to excel in direct and indirect competitions?

  • How do I select the best combination partner for my drug?

  • How do I predict the human PK of my small molecule or biologics?

  • How do I predict the objective response rate (ORR) for my novel (single or combination) treatment before it goes into the clinics?

  • How do I test my (novel) therapeutic concept in the clinics?

  • How do I select the efficacious dose of my compound (small molecule or biologics, either cytotoxic targeted therapy or immunotherapy) for the first-in-men study to verify proof of concept (PoC)?

  • How do I select the timing of biopsy to verify proof of principle (PoP)? 

  • How do I determine the required PD (target engagement and / or disease modulation) biomarker responses in clinical PoP study to indicate early sign of efficacy?

 

Problems cannot be solved by the same level of thinking that created them.

ALBERT EINSTEIN

 

CLEAR PATH TO SUCCESS

Immense value we bring in

Here are the 4 types of translational modelling we do to help our clients. We have concrete examples for each type of translation we have done in the past, and how modelling helped our clients to advance in drug projects. Due to confidentiality, we cannot disclose all details. Nevertheless, we are happy to discuss the rationales as much as we can to help you understand what is possible.

Scenario 1. In vitro data -> In vivo design 

Objective: Define the necessary compound profile to fulfil PoM criterion 

Value 

• Project direction: A valid target compound profile to support PoM study 

• Time saving: Shorter path to an in vivo experiment that demonstrates TGI

Case study: A DNA Damage Repair inhibitor model constructed using primarily in vitro data and some in vivo data successfully predicted the efficacy of a gapped schedule and a schedule with drug holidays. PDF 

Scenario 2. Clinical data -> In vivo monotherapy design 

Objective: Define and support PoC strategy (phases 1-2) 

Value

• Project direction: A valid target compound profile supported by clinical evidence 

• Time saving: Shorter path to an in vivo experiment supporting clinical feasibility

Scenario 3. Clinical data -> In vivo combination therapy design 

Objective: Identify the right combination partner to deliver clinical efficacy 

Value 

• Project direction: Selection of the best combination partner 

• Time saving: Shorter path to an in vivo experiment supporting clinical feasibility 

Scenario 4. In vivo data -> clinical design 

Objectives: Define PoP criterion (phase 1) 

Value

• Project direction: A valid clinical biomarker experiment design supported by preclinical science 

• Time saving: The right experiment indicating signs of clinical efficacy at the first time 

Medicinal chemist, computational chemist

• What is the target compound profile that meaningfully modulates the intended target?

• Do I need to optimise potency further or should I focus on DMPK properties?

• When does it make sense to nominate a compound for in vivo proof of mechanism?

Screening scientist

• How do I convince the management the target is validated?

• How do I maximise the value of my in vitro assay to inform in vivo studies?

Biomarker scientist

• Have I selected the right target engagement and pharmacology biomarkers?

• How much clinical biomarker changes (degree and duration) do I need to achieve meaningful pharmacological activity of the treatment?

Pharmacologist

• Does my data package confirm the therapeutic concept?

• What is a valid target compound profile supported by clinical evidence?

• Do I need a combination partner and why?

• What is the right combination partner to deliver clinical efficacy?

• What should be the target objective response criteria and why?

DMPK scientist

• How do I show my PK hypothesis is supported by different in vitroin vivo and clinical studies?

• How I do estimate human PK and eventually human dose for first-in-men study?

Project managers, investors

What is the minimum preclinical efficacy required to qualify the treatment as a potential clinical success?

Should I worry about the apparent inconsistency among different types of data?

• What is the one killer experiment I need to see to show this treatment excels in competition?

• Does the value proposition make sense?

PK/PD modeller, pharmacometrician

• Is the published model I am interested in good enough to support decision making?

• Which clinical studies can I use to design a meaningful translational studiy?

• Which is the most representative xenograft model for the intended disease treatment and why?

TALK TO US

Who are our typical clients? 

SIMPLE & TRANSPARENT

Our fee schedule

Our consulting basic day rate is £800 per day. This covers the services we provide, and additional charges may occur if certain software packages have to be purchased etc. 

 

We will always give an estimate up front and in writing when a project is scoped and agreed. If anything happens to cause us to increase our estimate, we will always let you know and get your approval before running up any additional time.

You will not incur fees until we both sign a written retainer agreement that spells out clearly (1) the services we will perform and (2) the fees we will charge. An advance retainer will normally be requested when we work on an hourly rate basis. However, if you change your mind and decide not to go through with a transaction before our work is completed, we total up the number of days we have actually worked at £800 per day, and rebate any excess to you promptly upon your request.

THE DIFFERENCE

What can you expect from us?

We pride ourselves in providing advanced mathematical and statistical modelling skills

  • Gained first-handed in the discovery and development of 3rd-generation EGFR inhibitor (osimertinib), DNA damage repair inhibitor (ATM inhibitor AZD0156) and a dozen more projects at AstraZeneca and Boehringer Ingelheim

  • Tested by successfully predicting many results of different in vitro, in vivo and clinical experiments

  • Saved AstraZeneca and Boehringer Ingelheim months of time for each preclinical oncology project supported

  • Convinced picky senior management of preclinical and clinical feasibility for investment decisions

  • Trusted by medicinal chemists, screening scientists, DMPK scientists, pharmacologists, biomarker scientists, translational scientists, PK/PD modellers, pharmacometricians, safety modellers, project managers

  • Awarded at AstraZeneca: Oncology Innovation Award and shortlisted for the Best Team of Year Award (2015)

  • Recognised by Boehringer Ingelheim: Repeated project breakthroughs attributed to modelling work for all supported projects during project reviews by Research Portfolio Committee (2016-2018)

We guarantee our contributions will

  • Save you from making costly mistakes that will cost you time, money, trust and career

  • Clarify requirements at each stage of drug discovery and development

  • Accelerate your drug projects by delivering compelling preclinical and clinical stories

  • Let you gain insights into the competition you are in

  • Help you make the best acquisition decisions

  • Fully realise the potential of your assets

  • Promote your leadership and company culture with a strong focus on data and clarity

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