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Feedback Inhibition00:46

Feedback Inhibition

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Biochemical reactions are occurring constantly in cells, converting starting substances to different products, usually with the help of enzymes that speed the reactions. Without enzymes, it would take far too long for most reactions to occur to be useful to the cell!
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Regulation of Sodium and Potassium01:26

Regulation of Sodium and Potassium

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The regulation of sodium and potassium ion concentrations in the human body is a complex process governed primarily by hormones such as aldosterone, antidiuretic hormone (ADH), and atrial natriuretic peptide (ANP).
Sodium Regulation
Sodium ions make up approximately 90% of extracellular cations, with a normal blood plasma concentration of 136–148 mEq/L. A decrease in blood volume and pressure triggers the release of renin from granular cells in the juxtaglomerular complex (JGC), primarily...
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Roles of Electrolytes: Sodium and Potassium01:24

Roles of Electrolytes: Sodium and Potassium

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Sodium plays a crucial role in maintaining fluid and electrolyte balance and overall bodily homeostasis. Sodium balance is primarily regulated by kidney function, which adjusts sodium elimination to match dietary intake and maintain proper electrolyte levels. Sodium is the most abundant cation in the extracellular fluid (ECF) and is found in salts such as sodium chloride (NaCl) and sodium bicarbonate (NaHCO3). Although cellular plasma membranes are relatively impermeable to sodium, its role in...
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Enzyme Inhibition01:30

Enzyme Inhibition

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Inhibitors are molecules that reduce enzyme activity by binding to the enzyme. In a normally functioning cell, enzymes are regulated by a variety of inhibitors. Drugs and other toxins can also inhibit enzymes. Some inhibitors bind to the enzyme’s active site, while others inhibit enzymatic activity by binding to other sites on the protein structure.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Updated: Jan 24, 2026

Modified Annexin V/Propidium Iodide Apoptosis Assay For Accurate Assessment of Cell Death
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Modified Annexin V/Propidium Iodide Apoptosis Assay For Accurate Assessment of Cell Death

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ナトリウム-ヨウ化物共輸送体阻害の評価のための統合モデリングアプローチ

Julia Kandler1, Ayse Sıla Kantarçeken1, Aljoša Smajić1

  • 1Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.

Journal of chemical information and modeling
|January 23, 2026
PubMed
まとめ

ナトリウム-ヨウ化物共輸送体（NIS）を阻害する環境化学物質は、発達神経毒性を引き起こす可能性があります。この研究では、毒性学的リスク評価の改善のためにNIS阻害を予測する、機械学習とドッキングを組み合わせた堅牢なインシリコフレームワークを開発しました。

キーワード:
ナトリウム-ヨウ化物共輸送体阻害発達神経毒性リスク評価機械学習ドッキング計算毒性学

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科学分野:

  • 毒性学
  • 計算化学
  • 神経科学

背景:

  • ナトリウム-ヨウ化物共輸送体（NIS）は甲状腺ホルモン合成に不可欠であり、脳の発達に重要です。
  • 環境化学物質によるNIS阻害は、自閉症やIQ低下などの神経発達障害につながる可能性があります。
  • 発達神経毒性（DNT）の正確な予測は、リスク評価に不可欠です。

研究 の 目的:

  • NIS阻害剤を予測するためのインシリコフレームワークを開発および検証すること。
  • NIS阻害をモデル化することにより、DNTの可能性のある環境化学物質を特定すること。
  • 次世代リスク評価戦略をサポートすること。

主な方法:

  • NIS阻害剤のドッキングベースの仮想スクリーニングを適用しました。
  • ECFP4およびCDDDを使用して機械学習モデル（RF、XGB、SVM）をトレーニングしました。
  • 9倍のクロスバリデーションと内部テストセットを使用してモデルを検証しました。

主要な成果:

  • 機械学習とドッキングの組み合わせ予測により、識別能力が向上しました（ROC AUC 0.77）。
  • 最適な閾値は、MCC 0.32およびバランス精度0.78を生成しました。
  • 1412個の多様な化合物を使用して堅牢なフレームワークが開発されました。

結論:

  • この研究は、NIS阻害を予測するための新規で堅牢な計算フレームワークを提示します。
  • このアプローチは、DNTを引き起こす化学物質の同定を強化します。
  • 開発された方法は、毒性学的リスク評価のための新しいアプローチを表します。