A decade of machine learning-based predictive models for human pharmacokinetics : Advances and challenges
Copyright © 2021 Elsevier Ltd. All rights reserved..
Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
|---|---|
| Enthalten in: |
Drug discovery today - 27(2022), 2 vom: 01. Feb., Seite 529-537 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Danishuddin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Chemical Big Data |
|---|
| Anmerkungen: |
Date Completed 20.04.2022 Date Revised 20.04.2022 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.drudis.2021.09.013 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM331339005 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM331339005 | ||
| 003 | DE-627 | ||
| 005 | 20231225213238.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.drudis.2021.09.013 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1104.xml |
| 035 | |a (DE-627)NLM331339005 | ||
| 035 | |a (NLM)34592448 | ||
| 035 | |a (PII)S1359-6446(21)00407-4 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Danishuddin |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A decade of machine learning-based predictive models for human pharmacokinetics |b Advances and challenges |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 20.04.2022 | ||
| 500 | |a Date Revised 20.04.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Chemical Big Data | |
| 650 | 4 | |a Drug development | |
| 650 | 4 | |a Pharmacokinetics | |
| 650 | 4 | |a QSAR | |
| 700 | 1 | |a Kumar, Vikas |e verfasserin |4 aut | |
| 700 | 1 | |a Faheem, Mohammad |e verfasserin |4 aut | |
| 700 | 1 | |a Woo Lee, Keun |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Drug discovery today |d 1998 |g 27(2022), 2 vom: 01. Feb., Seite 529-537 |w (DE-627)NLM095449159 |x 1878-5832 |7 nnns |
| 773 | 1 | 8 | |g volume:27 |g year:2022 |g number:2 |g day:01 |g month:02 |g pages:529-537 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.drudis.2021.09.013 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 27 |j 2022 |e 2 |b 01 |c 02 |h 529-537 | ||