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6MWTにおける変動性を理解する:DMD病態進行モデルの開発

Duchenne Muscular Dystrophy (DMD) is a life-threatening, sex-linked, pediatric rare disease, primarily affecting boys. 進行性の筋変性、筋力低下をきたし、最終的には機能損失につながるのが特徴です。DMDは、筋肉の統合性の維持、および筋組織の分化を促すシグナルと成長の促進に必要なタンパク質である、ジストロフィン遺伝子の変異が原因となり発症します。患児は4~5歳でDMDの診断を受けます。By age twelve to fourteen, patients lose the ability to walk. 最終的には、患者は 20 歳代の半ばで心筋症や呼吸機能障害などの合併症により死に至ります。

Corticosteroids are the standard for treating symptoms and can delay loss of ambulation. More recently dystrophin-restoring treatments such as exon skipping therapies, stop codon-read-through, and gene therapies hold promise to increase DMD patients’ life expectancies. However, more research is still needed to better understand the mechanisms of muscle atrophy and defects to develop more efficacious therapies.

Because DMD is a pediatric disease, there is a developmental component to how children perform in motor function tests including the 6-minute walk test (6MWT) that are used to assess drug effects. より低い年齢層の患者は、時間とともにある程度の運動機能の改善を示します。しかし、症状が進行するとともに運動機能は低下します。したがって、6MWTの結果は「改善と悪化」の両方を示します。年齢依存性は、医薬品の試験を評価するうえで重要です。なぜなら、年齢依存性を考慮しないことで、薬物効果が正しく評価されず、薬剤の有効性について誤った結論が導き出される可能性があるからです。

In a recent webinar with Dr. Joga Gobburo (University of Maryland), I reviewed how a disease progression model, developed using Phoenix NLME, was used to better understand variability in the 6MWT. The project sought to determine if a quantitative approach can discern potential drug effects on the 6MWT trajectory given the age-dependent variability. 病態進行モデルの活用によって、その後に続く試験のデザインに有用な情報の収集や、薬物効果の評価、そして将来実施される試験における潜在的な薬物効果の予測が可能であるか検証が実施されました。

Disease progression modeling for rare diseases is limited by data availability and often data from the literature is initially used. Although aggregate-level information on age, race and weight is typically summarized in the literature, the lack of detailed patient demographic information that drives variability, such as type of steroid treatment, and patient factors including dystrophin mutation, baseline cardiac and respiratory function, may hinder building a disease progression model.

Six structural models with increasing complexity were evaluated for their ability to accurately predict the training dataset and a novel dataset obtained from the literature of previously published DMD clinical trials. さまざまな種類の6MWTにおける変動を包括的に解析するために、Phoenix NLMEにおいて同時推定を実行する線型モデルが構築されました。The model was a series of linear models that did not require age to be fixed a priori but rather could let the data speak for itself and estimate the age dependence of the variability. The disease progression linear model did a reasonable job of accounting for variability seen in the observed training datasets and for predicting 6MWT performance in the training datasets as well as a novel dataset.

To evaluate a drug for a clinical meaningful effect, a standard sample size calculation was performed to determine how many patients would be needed to detect a drug effect when using the 6MWT as a clinical endpoint. 総計の年齢層の集団を対象に試験を行う場合、治療群ごとに 160 名の被験者を募集することが必要となることが示されました。この人数は、DMDのような希少疾患では不可能な人数です。しかし、病態進行モデルを活用することで、年齢で層別化されていない少人数の試験であっても、治療群ごとに 6 名という少数の症例数で開発薬物の効果を検出できる可能性があります。The model was shown to parse out the age dependence on variability and predict the dose response for the hypothetical drug.

This approach highlighted the power of modeling and simulation (M&S) for rare disease drug development. It demonstrated how a quantitative platform can be used to simulate different drug trial scenarios to assess sources of patient variability with the potential to allow for much smaller drug trials to be conducted.

To learn more about this project, please click the link below to watch the recorded webinar. A special thanks to Drs. Joga Gobburo and Suzanne Minton for helping to prepare and execute the webinar.

筆者について

By: Lora Hamuro

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