About the author of this work
- Ph.D. in Pharmacometrics from the University of Uppsala, Sweden. M.Sc. in Medical Statistics, B.Sc. in Mathematics, University of Southampton, UK.
- Has worked as an expert pharmacometrics consultant for over 20 years, advising pharma companies in the design and analysis of clinical studies across all phases of clinical development.
- Expert in Model Based Meta Analyses (MBMA), primarily in type 2 diabetes.
- Expert in optimal (adaptive) designs for Dose-Exposure-Response studies (typically in Phase 2), for all types of data (normal, binary, categorical, count, survival etc.).
- Keen to improve the way we do drug development !
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The following paper was published in the highly regarding Journal of Clinical Pharmacology and Therapeutics. The title (with link to PDF), citation and abstract are shown below.
A New Paradigm. "Learn - Learn More"; Dose-Exposure-Response at the Center of Drug Development and Regulatory Approval.
Maloney A.
Clin Pharmacol Ther. 2017 Dec;102(6):942-950. doi: 10.1002/cpt.710
In his seminal paper, Lewis Sheiner introduced the “Learning versus Confirming” paradigm. From that foundation, this work proposes why the precise estimation of the dose-exposure-response (D-E-R) for both efficacy and safety endpoints should be the ultimate goal for most drug development programs. The subsequent identification and approval of an optimal dose regimen range will provide a pragmatic framework for delivering personalized medicine based on dose titration for each and every patient.
- Always strives to do work of the highest quality. People appreciate quality!
- Lives in Sweden with his wife and two children.
- This website? An experimental project to see if I could build something with my very basic website skills. My real work is much more professional!
Alan Maloney, PhD
About this project
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Using publicly available data, the goal was to compare all # type 2 diabetes drugs across 3 key endpoints (HbA1c, weight and hypoglycemia), and share the results with the diabetes community.
# Thus far, we have over 40 drugs from 9 different drug classes. These drugs represent all therapies recommended by the American Diabetes Association as either first, second or third line therapies. - This is hopefully a Win-Win-Win project. Patients/Physicians get access to the most accurate and unbiased comparisons of the different drug regimens. The pharmaceutical companies can run smarter studies, and better identify what projects should be fully resourced, and what projects could be terminated. Lastly, I hope to enjoy applying this work to interesting projects (thus enabling additional classes/drugs/studies/endpoints to be incorporated), and know I have contributed, in a very small way, to help improve patient outcomes around the world.
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Over the last 20 years, I have conducted numerous MBMA's in type 2 diabetes, normally for a single endpoint (like HbA1c), for a single drug class. Since MBMA is significantly more complex and useful than standard (network) meta analysis, each project would take a minimum of 4-6 months, but were often extended and expanded in scope to link the results with internal drug development plans and strategies. As the work was considered important and valuable, it remained confidential. After seeking permissions, this project was undertaken.
- A complete review of over 5500 publications and clinical trials conducted in type 2 diabetes over the last 20 years.
- Current database (as of 30 August 2020) includes:
- 307 unique studies
- 958 unique treatment arms
- > 170,000 patients
- 34 approved drugs + 11 experimental drugs (efpeglenatide, ITCA 650, daily semaglutide (SC), tirzepatide, NN0090-2746, MEDI0382, ipragliflozin, sotagliflozin, tofogliflozin, omarigliptin and teneligliptin).
- Advanced mathematical models for HbA1c, weight and hypoglycemic risk have been developed.
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- Describe and quantify the dose response of each drug.
- Describe and quantify the time course of how each endpoint changes as a function of time and drug class.
- Reflect clinical pharmacology. That is, how drugs within a drug class are likely to share common dose response profiles.
- Reflect physiological aspects. For example, how HbA1c changes are dependent on baseline HbA1c, with larger changes both expected and seen for higher baselines.
- Use the best estimation method available (Bayesian Monte Carlo Markov Chain), ensuring we accurately 'map' the joint distribution of the model parameters, rather than rely upon approximate methods such as maximum likelihood.
- The data and models are the best available in the world, and can be used to predict any treatment regimen versus any other treatment regimen for any study design scenario.
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On our home page, we used a standard design of 26 weeks, baseline HbA1c of 65 mmol/mol (8.1%), drug naive patients, versus placebo. Study designs (i.e. duration, sample size, patient population, choice of comparator etc.) can then be optimised to maximise the probability of success for a given cost.
- The project has been successfully extended to include and predict (initial) combination therapy. Future plans include incorporating new drugs (e.g. TPP399), adding more basal insulin studies, alpha-glucosidase inhibitors, meglitinides, as well as additional endpoints (e.g. FPG, SBP, DBP, GI side effects etc.).
Model-Based Meta-Analysis (MBMA) in Type 2 Diabetes
Background:
Project Overview:
About the future!
- Personalized medicine is about getting the right drug for each patient. With multiple treatment regimens available, identifying which patients will respond best to a particular regimen is paramount, but this is far from simple. The use of current and novel biomarkers, combined with patient level covariates (e.g. pharmacogenetics), will better identify patients who will benefit the most for a given drug.
- After identifying a drug, we need to find the right dose for each patient. The use of current and novel diagnostics for glucose monitoring will enable physicians and patients to better guide individual dose titration, so as to maximise efficacy and minimise safety risks. Our drug labels will need to change to better facilitate individual dose titration.
- Improved drug development programs that focus on maximising individual patient outcomes, not on finding a 'single' one-size-fits-all dose.
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Patients are different, and optimal doses for patients can be very different (i.e. we have very large inter-individual variability (IIV)...think 10-15 fold dose ranges, not 2-3 fold dose ranges). Whilst the importance and value of insulin titration has been well understood, dosing for many other regimens has sought the 'one-size-fits-all' dose. The use of novel designs to find the best titration strategy (rather than the best 'average' dose) has the potential to revolutionise how we develop and approve new antihyperglycemic drugs. Fundamentally, we will get better outcomes for our patients.
- With evermore treatment regimens available, patients will appreciate both the convenience and efficacy of 'polypill' regimens.
- Reimbursement based on clinical outcomes, not the cost of the pill!
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Tying reimbursement to outcomes could well be the best pricing model for drugs in the future. The outgoing CEO of Novartis (Joe Jimenez) was an advocate, and with an ever improving ability to track and analyse real world evidence of effectiveness, the technology and science are sufficiently advanced to make it happen. For example, why not directly link price to the achieved HbA1c reduction (in mmol/mol) at 6 months (e.g. $ amount * mmol/mol)? In addition to sharing risk between the pharma company and the payer, it would stimulate pharma to better focus on tailoring individual dosing regimens to maximise individual patient outcomes, rather than accept a 'mean' outcome for a standardised dose. That is, the development focus will be about personalised medicine/dosing as outlined above.