Revisiting the application of Immobilized Artificial Membrane (IAM) chromatography to estimate in vivo distribution properties of drug discovery compounds based on the model of marketed drugs

Valkó, Klára; Rava, Silvia; Bunally, Shenaz; Anderson, Scott

doi:10.5599/admet.757

Cited by 12 publications

(10 citation statements)

References 23 publications

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“…Since, however, electrostatic interactions have a strong contribution in retention mechanism, especially in the case of protonated bases, IAM chromatography is considered as also reflecting drug-membrane interactions and tissue binding [23,24]. IAM models for oral absorption, skin partitioning, and brain penetration, mostly expressed as logBB, have been reported in the literature, usually in combination with additional molecular descriptors [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Classification of CNS + and CNS − drugs has been suggested using the IAM retention factor divided by the fourth root of molecular weight [43].…”

Section: Introductionmentioning

confidence: 99%

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

2022

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The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood–brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.

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Section: Introductionmentioning

confidence: 99%

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

2022

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“…Compound 38 demonstrated a more than fourfold improvement in maximum drug efficiency percentage (DE max ) relative to the naphthalene 4 , which predicts an improvement in free plasma concentration. Similarly, the volume of distribution ( V d ) of 38 is predicted to be larger than that of 4 . Optimizing the physicochemical properties of the compounds is relevant to their cellular permeability, which could be improved and also to the potential utility of the compounds in interventions in neurological conditions where the role of Nrf2 induction has a number of promising applications. ,,, …”

Section: Results and Discussionmentioning

confidence: 97%

Phenyl Bis-Sulfonamide Keap1-Nrf2 Protein–Protein Interaction Inhibitors with an Alternative Binding Mode

et al. 2022

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Inhibitors of Kelch-like ECH-associated protein 1 (Keap1) increase the activity of the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) by stalling its ubiquitination and degradation. This enhances the expression of genes encoding proteins involved in drug detoxification, redox homeostasis, and mitochondrial function. Nrf2 activation offers a potential therapeutic approach for conditions including Alzheimer’s and Parkinson’s diseases, vascular inflammation, and chronic obstructive airway disease. Non-electrophilic Keap1-Nrf2 protein–protein interaction (PPI) inhibitors may have improved toxicity profiles and different pharmacological properties to cysteine-reactive electrophilic inhibitors. Here, we describe and characterize a series of phenyl bis-sulfonamide PPI inhibitors that bind to Keap1 at submicromolar concentrations. Structural studies reveal that the compounds bind to Keap1 in a distinct “peptidomimetic” conformation that resembles the Keap1-Nrf2 ETGE peptide complex. This is different to other small molecule Keap1-Nrf2 PPI inhibitors, including bicyclic aryl bis-sulfonamides, offering a starting point for new design approaches to Keap1 inhibitors.

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“…The total plasma protein binding, lung tissue binding, drug efficiency, brain tissue binding and brain to blood ratio, cell partition coefficient and lung tissue binding have been calculated using the equations listed in Table 5 and are shown in Table 6. = elog k(HSA) log k HSA [30] = log (%HSAbound/(101-%HSA bound)) Estimated log V d [43,44] = 0.44*log K IAM -0.22*log K HSA -0.62 Estimated log V du [27] = 0.23*log K HSA +0.43*log K IAM -0.72 DE max [45] = 100/V du log k BTB [31] = 1.29*log k IAM+1.03*log k HSA-2.37 log k (PPB) [31] = 0.98 * log +0.19 * log +0.031 * CHI log D 7.4−0.20 %BTB [31] = 100*10log k BTB/(1+10log k BTB) %PPB [31] = 100*10log k PPB/(1+10log k PPB) f u BTB and PPB [31] = (100-%BTB)/100 and (100 %-%PPB)/100 K bb [31] = f u PPB/f u BTB log K pcell [46] = 1.1log k IAM-1.9 log k LTB [31] = 0.49*log k PPB+0.34CHIlog D-0.069 % LTB [31] = 100*10 logkLTB /(1+10 logkLTB )…”

Section: Resultsmentioning

confidence: 99%

Biomimetic properties and estimated in vivo distribution of chloroquine and hydroxy-chloroquine enantiomers

Valkó¹,

Zhang²

2021

ADMET DMPK

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Chloroquine and hydroxy-chloroquine already established as anti-malarial and lupus drugs have recently gained renewed attention in the fight against the Covid-19 pandemic. Bio-mimetic HPLC methods have been used to measure the protein and phospholipid binding of the racemic mixtures of the drugs. The tissue binding and volume of distribution of the enantiomers have been estimated. The enantiomers can be separated using Chiralpak AGP HPLC columns. From the α-1-acid-glycoprotein (AGP) binding, the lung tissue binding can be estimated for the enantiomers. The drugs have a large volume of distribution, showed strong and stereoselective glycoprotein binding, medium-strong phospholipid-binding indicating only moderate phospholipidotic potential, hERG inhibition and promiscuous binding. The drug efficiency of the compounds was estimated to be greater than 2 % which indicates a high level of free biophase concentration relative to dose. The biomimetic properties of the compounds support the well-known tolerability of the drugs.

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Revisiting the application of Immobilized Artificial Membrane (IAM) chromatography to estimate in vivo distribution properties of drug discovery compounds based on the model of marketed drugs

Cited by 12 publications

References 23 publications

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

Phenyl Bis-Sulfonamide Keap1-Nrf2 Protein–Protein Interaction Inhibitors with an Alternative Binding Mode

Biomimetic properties and estimated in vivo distribution of chloroquine and hydroxy-chloroquine enantiomers

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