Poly(DL-lactide-co-glycolic acid) Nanoparticle Design and Payload Prediction : A Molecular Descriptor Based Study
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- Das Suvadra
- Department of Chemical Technology, University of Calcutta
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- Roy Partha
- Department of Chemical Technology, University of Calcutta
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- Islam Md Ataul
- Department of Chemical Technology, University of Calcutta
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- Saha Achintya
- Department of Chemical Technology, University of Calcutta
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- Mukherjee Arup
- Department of Chemical Technology, University of Calcutta
書誌事項
- タイトル別名
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- Poly(<small>DL</small>-lactide-<i>co</i>-glycolic acid) Nanoparticle Design and Payload Prediction: A Molecular Descriptor Based Study
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説明
Polymer nanoparticles are veritable tools for pharmacokinetic and therapeutic modifications of bioactive compounds. Nanoparticle technology development and scaling up are however often constrained due to poor payload and improper particle dissolution. This work was aimed to develop descriptor based computational models as prior art tools for optimal payload in polymeric nanoparticles. Loading optimization experiments were carried out both in vitro and in-silico. Molecular descriptors generated in three different platforms DRAGON, molecular operating environment (MOE) and VolSurf+ were used. Multiple linear regression analysis (MLR) provided computation models which were further validated based on goodness of fit statistics and correlation coefficients (DRAGON, R2=0.889, Q2=0.657, R2pred=0.616; MOE, R2=0.826, Q2=0.572, R2pred=0.601; and VolSurf+, R2=0.818, Q2=0.573, R2pred=0.653). Pharmacophore space modeling studies were carried out in order to understand the fundamental molecular interactions necessary for drug loading in poly(DL-lactide-co-glycolic acid). The space modeling study (R2=0.882, Q2=0.662, R2pred=0.725, Δcost=108.931) indicated that hydrogen bond acceptors and ring aromatic features are of primary significance for nanoparticle drug loading. Results of in vitro experiments have also confirmed the fact as a viable prognosis in case of nanoparticle payload. Polymeric nanoparticles payload prediction can therefore be a useful tool for wider benefits at the preformulation stages itself.
収録刊行物
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- CHEMICAL & PHARMACEUTICAL BULLETIN
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CHEMICAL & PHARMACEUTICAL BULLETIN 61 (2), 125-133, 2013
公益社団法人 日本薬学会
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詳細情報 詳細情報について
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- CRID
- 1390001204176998528
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- NII論文ID
- 130002460108
- 40019558488
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- NII書誌ID
- AA00602100
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- COI
- 1:STN:280:DC%2BC3s7ns1KqsA%3D%3D
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- ISSN
- 13475223
- 00092363
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- NDL書誌ID
- 024220120
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- PubMed
- 23196343
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- PubMed
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可