Designing to Accelerate Translation (DART) of Emerging Health - - PowerPoint PPT Presentation

Designing to Accelerate Translation (DART)

• f Emerging Health Innovations

Alex T. Ramsey, Ph.D.

Department of Psychiatry Washington University School of Medicine

Department of Psychiatry

What’s on the agenda?

Introducing the DART concept Alex Ramsey Personalized genomics-informed smoking cessation treatment Alex Ramsey Self-management via automated telehealth and mHealth Stephen Bartels The opioid epidemic: Dire need for a rapid scale up Mark McGovern Panel Discussant Enola Proctor

Department of Psychiatry

What’s behind the idea of DART?

Key Premise #1 Translation of evidence to practice is unnecessarily slow.

Hot-Take #1 D&I research should not be viewed merely as a final step in the translational process. Hot-Take #2 Without radically different approaches to accelerating translation, diffusion of evidence to practice will remain slow.

Key Premise #2 Translation of evidence to practice is a dynamic process.

Hot-Take #3 Researchers are responsible for considering implementation needs “early and often”. Hot-Take #4 All health research should aim to address an actual problem or need, with an expectation of ongoing iterative improvements. Hot-Take #5 Much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation.

Department of Psychiatry

Is evidence of efficacy all that matters?

• P = f[E(D + R)] / C
• Pace of translation (P) as a function of:
• strength of evidence (E) – efficacy
• demand (D) – urgency, existing alternatives, stakeholder pull
• risk (R) – potential clinical harms, risk from not acting on available evidence
• cost (C) – financial expense, resource intensiveness, disruptive effects

Department of Psychiatry

Hypothesized Pace of Translation – 4 Typologies of Innovations

Low Risk High Risk Low Demand High Demand Pace Time

innovation

Department of Psychiatry

Hypothesized Pace of Translation – 4 Typologies of Innovations

Mis-implemented State Optimal State

Value Lost Misuse of Resources Low Risk High Risk Low Demand High Demand Pace Time

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DART: A Conceptual Architecture for Acceleration How can we practice DART?

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DART Strategies to Optimize Implementation

Current State: “Where We Are” Optimal State: “Where We Want to Be” Improvement Strategies: “What It Will Take” Meaningful Stakeholder Partnership

Research siloes Team science Focus on the big picture and engage trans-disciplinary efforts Restrictive samples Citizen science Harness power of public for scientific activities Disconnected from industry Partnering with industry Partner with those primed to bring innovations to market

Design Innovations for D&I

Pushing out innovations Eliciting / meeting user demand Understand user motives and context; demonstrate value add Researcher-driven Human-centered design Involve diverse group of end-users throughout development Efficacy over effectiveness Robust, context-sensitive innovations Pragmatic and adaptive trials to optimize adoption potential

Learning Healthcare System

Narrow use of evidence Ongoing / efficient evidence review Use existing data, rapid reviews, and Create-Trial-Sustain models Static delivery systems Using iterative feedback Give real-time feedback on key outcomes to providers Resistant to change Nimble, change-oriented mindset Train workforce in core concepts that apply across technologies

Department of Psychiatry

Synergies between Implementation Science, Learning Health Care Systems, and Precision Medicine

Chambers et al JAMA 2016

Fast-tracking the future: Advancing the translation of personalized genomics-informed smoking cessation treatment

Alex T. Ramsey, PhD, Ami Chiu, BS, Li-Shiun Chen, MD, MPH, ScD, Laura Bierut, MD

Department of Psychiatry Washington University School of Medicine

Department of Psychiatry

Applications of genomic information for smoking

Application Key Questions

Identifying elevated disease risk in individuals who smoke What is my likelihood of severe nicotine dependence and lung cancer? Personalizing treatment

• f smoking cessation

What are my odds of quitting without medication? How likely will I benefit from the nicotine patch? Motivating behavior change and smoking cessation How much cancer risk can be reduced if I quit smoking?

Ramsey et al 2018 Transl Beh Med

Department of Psychiatry

Chain of evidence for genomic applications and smoking- related outcomes

Category of Evidence Adequacy of Evidence

Analytic Validity

How accurately does the genetic test measure the biomarker?

Convincing: All elements of analytic validity indicate exceptionally high reliability and validity of genetic testing

Clinical Validity

How strongly is biomarker associated with disease or treatment response?

Adequate: Nicotine use disorder is common and highly heritable; CHRNA5 variant has been found through multiple GWAS to be associated with smoking heaviness, several smoking-related diseases and mortality across populations

Clinical Utility

How useful is the test in improving clinical care or health behaviors?

Inadequate: Evidence lacking but strong premise: test results are potentially actionable in both clinical and direct-to- consumer contexts; reach is increasing, and cost is decreasing Ramsey et al 2018 Transl Beh Med

Department of Psychiatry

Another look at the DART formula: P = f[E(D + R)] / C

Evidence Demand Risk Cost Moderate

Strong Analytic and Clinical Validity; Clinical Utility needed

High

More than 2 million people genotyped for direct-to-consumer genetic testing

Low

After receiving genetic results: Never smokers do not start smoking Former smokers do not restart No increase in anxiety or depression

Decreasing

Genome array is < $200 Sequencing is < $1000

Ramsey et al 2018 Transl Beh Med Hartz et al 2015 Genet Med Olfson et al 2016 NTR

Genomic applications for smoking cessation may be a “Fast Track” innovation

Department of Psychiatry

A Closer Look at Clinical Utility

• Using clinically-valid genomic applications to inform medication response

can optimize treatment

• Implementation will require acceptability and usefulness among smokers
• We examined:

(1) consumer demand for genetic testing for health risks and nicotine dependence, (2) desire to take smoking cessation medication when hypothetical genetic results predict the pharmacogenetic medication response, and (3) receipt of returned genetic results.

Department of Psychiatry

Methods

• Participants (n=1515):
• Current smokers from a genetic nicotine dependence study (n=1306) and
• Current smokers from a smoking cessation trial (n=209)
• Procedure:
• Survey on desire to receive health- and smoking-related genetic testing results
• Another subset (n=474) was surveyed on the desire to take medication given

hypothetical below- or above-average pharmacogenetic medication responses

• A subset of current smokers (n=705) was given the opportunity to access personalized

genetic results online

Department of Psychiatry

Current smokers desire genetic testing

Chiu et al. 2018 J Neuroimmune Pharmacology

Department of Psychiatry

Current smokers desire genetically-efficacious medication

Chiu et al. 2018 J Neuroimmune Pharmacology

Department of Psychiatry

Current smokers desire genetically-efficacious medication

Chiu et al. 2018 J Neuroimmune Pharmacology

Department of Psychiatry

Despite high interest, receipt of results was low

• Among subset of current smokers (n=705) given the opportunity to

access personalized genetic results online, only 189 (27%) accessed the results.

• Smokers more likely to access results included Caucasians, women,

and those with a high school diploma and household income above federal poverty level.

Department of Psychiatry

Implications

• High demand for genomics-informed smoking cessation treatment.
• A positive expected pharmacogenetic response to smoking cessation medication

increases desire to take medication.

• These data provide insights on the demand for, acceptability, and clinical utility of

smoking-related genomic applications, key drivers of accelerated translation of emerging precision medicine innovations.

• Dedicated efforts are needed to address barriers to access.
• Studying the implementation of genomics-informed treatment in clinical settings

represents a key next step.

Department of Psychiatry

Linking the chain of evidence for genomic innovations to advance genomics translation

Ramsey et al 2018 Transl Beh Med

Department of Psychiatry

Thank You!

Alex T. Ramsey, PhD aramsey@wustl.edu

Ramsey: NIDA K12DA041449 Chen: NCI P01CA089392 NIDA R01DA038076 Bierut: NIDA R01DA036583 CTSA UL1TR002345 NCI P30CA091842