Predictive performance of next generation human physiologically based kinetic (PBK) model predictions based on in vitro and in silico input data Article Swipe
Ans Punt
,
Jochem Louisse
,
Karsten Beekmann
,
Nicole Pinckaers
,
Eric Fabian
,
Bennard van Ravenzwaay
,
Paul L. Carmichael
,
Ian Sorrell
,
Thomas E. Moxon
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14573/altex.2108301
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14573/altex.2108301
The goal of the present study was to assess the predictive performance of a generic human physiologically based kinetic (PBK) model based on in vitro and in silico input data and the effect of using different input approaches for chemical parameterization on those predictions. For this purpose, a dataset was created of 38,772 Cmax predictions for 44 compounds by applying different combinations of in vitro and in silico approaches for chemical parameterization, and these predicted Cmax values were compared to reported in vivo data. Best results were achieved when the hepatic clearance was parameterized based on in vitro (i.e., hepatocytes or liver S9) measured intrinsic clearance values, the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients, and the method of Lobell and Sivarajah for calculating the fraction unbound in plasma. With these parameters, the median Cmax values of 34 out of the 44 compounds were predicted within 5-fold of the observed Cmax, and the Cmax values of 19 compounds were predicted within 2-fold. The median Cmax values of 10 compounds were more than 5-fold overestimated. Underestimations (> 5-fold) did not occur. A comparison of the current generic PBK model structure with chemical-specific PBK models available in literature was made to identify possible kinetic processes not included in the generic PBK model that might explain the overestimations. Overall, the results provide crucial insights into the predictive performance of PBK models based on in vitro and in silico input and the influence of different input approaches on the model predictions.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14573/altex.2108301
- OA Status
- gold
- Cited By
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4206917753
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4206917753Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14573/altex.2108301Digital Object Identifier
- Title
-
Predictive performance of next generation human physiologically based kinetic (PBK) model predictions based on in vitro and in silico input dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Ans Punt, Jochem Louisse, Karsten Beekmann, Nicole Pinckaers, Eric Fabian, Bennard van Ravenzwaay, Paul L. Carmichael, Ian Sorrell, Thomas E. MoxonList of authors in order
- Landing page
-
https://doi.org/10.14573/altex.2108301Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.14573/altex.2108301Direct OA link when available
- Concepts
-
In silico, Cmax, In vivo, Biological system, In vitro, Parameterized complexity, Chemistry, Mathematics, Biology, Pharmacokinetics, Biochemistry, Pharmacology, Biotechnology, Algorithm, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 7, 2023: 4, 2022: 6Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4206917753 |
|---|---|
| doi | https://doi.org/10.14573/altex.2108301 |
| ids.doi | https://doi.org/10.14573/altex.2108301 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/35064272 |
| ids.openalex | https://openalex.org/W4206917753 |
| fwci | 5.26695527 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D007700 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Kinetics |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D008099 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Liver |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D008954 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Models, Biological |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D006801 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Humans |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D007700 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Kinetics |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D008099 |
| mesh[6].is_major_topic | True |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Liver |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D008954 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Models, Biological |
| type | article |
| title | Predictive performance of next generation human physiologically based kinetic (PBK) model predictions based on in vitro and in silico input data |
| biblio.issue | 2 |
| biblio.volume | 39 |
| biblio.last_page | 221–234 |
| biblio.first_page | 221–234 |
| topics[0].id | https://openalex.org/T10211 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9952999949455261 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1703 |
| topics[0].subfield.display_name | Computational Theory and Mathematics |
| topics[0].display_name | Computational Drug Discovery Methods |
| topics[1].id | https://openalex.org/T10570 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9697999954223633 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2730 |
| topics[1].subfield.display_name | Oncology |
| topics[1].display_name | Drug Transport and Resistance Mechanisms |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2157 |
| apc_paid.value | 2000 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2157 |
| concepts[0].id | https://openalex.org/C2775905019 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9252058267593384 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q192572 |
| concepts[0].display_name | In silico |
| concepts[1].id | https://openalex.org/C22979827 |
| concepts[1].level | 3 |
| concepts[1].score | 0.8487138748168945 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5137311 |
| concepts[1].display_name | Cmax |
| concepts[2].id | https://openalex.org/C207001950 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5226202607154846 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q141124 |
| concepts[2].display_name | In vivo |
| concepts[3].id | https://openalex.org/C186060115 |
| concepts[3].level | 1 |
| concepts[3].score | 0.47500598430633545 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q30336093 |
| concepts[3].display_name | Biological system |
| concepts[4].id | https://openalex.org/C202751555 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4735260009765625 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q221681 |
| concepts[4].display_name | In vitro |
| concepts[5].id | https://openalex.org/C165464430 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4289632737636566 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1570441 |
| concepts[5].display_name | Parameterized complexity |
| concepts[6].id | https://openalex.org/C185592680 |
| concepts[6].level | 0 |
| concepts[6].score | 0.40367841720581055 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[6].display_name | Chemistry |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.3559027910232544 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C86803240 |
| concepts[8].level | 0 |
| concepts[8].score | 0.24657994508743286 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[8].display_name | Biology |
| concepts[9].id | https://openalex.org/C112705442 |
| concepts[9].level | 2 |
| concepts[9].score | 0.22165724635124207 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q323936 |
| concepts[9].display_name | Pharmacokinetics |
| concepts[10].id | https://openalex.org/C55493867 |
| concepts[10].level | 1 |
| concepts[10].score | 0.2163316309452057 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[10].display_name | Biochemistry |
| concepts[11].id | https://openalex.org/C98274493 |
| concepts[11].level | 1 |
| concepts[11].score | 0.17237144708633423 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q128406 |
| concepts[11].display_name | Pharmacology |
| concepts[12].id | https://openalex.org/C150903083 |
| concepts[12].level | 1 |
| concepts[12].score | 0.14007988572120667 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7108 |
| concepts[12].display_name | Biotechnology |
| concepts[13].id | https://openalex.org/C11413529 |
| concepts[13].level | 1 |
| concepts[13].score | 0.11222726106643677 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[13].display_name | Algorithm |
| concepts[14].id | https://openalex.org/C104317684 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[14].display_name | Gene |
| keywords[0].id | https://openalex.org/keywords/in-silico |
| keywords[0].score | 0.9252058267593384 |
| keywords[0].display_name | In silico |
| keywords[1].id | https://openalex.org/keywords/cmax |
| keywords[1].score | 0.8487138748168945 |
| keywords[1].display_name | Cmax |
| keywords[2].id | https://openalex.org/keywords/in-vivo |
| keywords[2].score | 0.5226202607154846 |
| keywords[2].display_name | In vivo |
| keywords[3].id | https://openalex.org/keywords/biological-system |
| keywords[3].score | 0.47500598430633545 |
| keywords[3].display_name | Biological system |
| keywords[4].id | https://openalex.org/keywords/in-vitro |
| keywords[4].score | 0.4735260009765625 |
| keywords[4].display_name | In vitro |
| keywords[5].id | https://openalex.org/keywords/parameterized-complexity |
| keywords[5].score | 0.4289632737636566 |
| keywords[5].display_name | Parameterized complexity |
| keywords[6].id | https://openalex.org/keywords/chemistry |
| keywords[6].score | 0.40367841720581055 |
| keywords[6].display_name | Chemistry |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.3559027910232544 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/biology |
| keywords[8].score | 0.24657994508743286 |
| keywords[8].display_name | Biology |
| keywords[9].id | https://openalex.org/keywords/pharmacokinetics |
| keywords[9].score | 0.22165724635124207 |
| keywords[9].display_name | Pharmacokinetics |
| keywords[10].id | https://openalex.org/keywords/biochemistry |
| keywords[10].score | 0.2163316309452057 |
| keywords[10].display_name | Biochemistry |
| keywords[11].id | https://openalex.org/keywords/pharmacology |
| keywords[11].score | 0.17237144708633423 |
| keywords[11].display_name | Pharmacology |
| keywords[12].id | https://openalex.org/keywords/biotechnology |
| keywords[12].score | 0.14007988572120667 |
| keywords[12].display_name | Biotechnology |
| keywords[13].id | https://openalex.org/keywords/algorithm |
| keywords[13].score | 0.11222726106643677 |
| keywords[13].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.14573/altex.2108301 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210171861 |
| locations[0].source.issn | 1868-596X, 1868-8551 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1868-596X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | ALTEX |
| locations[0].source.host_organization | https://openalex.org/P4310319965 |
| locations[0].source.host_organization_name | Springer Nature |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319965 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | ALTEX |
| locations[0].landing_page_url | https://doi.org/10.14573/altex.2108301 |
| locations[1].id | pmid:35064272 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | ALTEX |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/35064272 |
| locations[2].id | pmh:oai:library.wur.nl:wurpubs/596859 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400096 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Wageningen University and Researchcenter Publications (Wageningen University & Research) |
| locations[2].source.host_organization | https://openalex.org/I913481162 |
| locations[2].source.host_organization_name | Wageningen University & Research |
| locations[2].source.host_organization_lineage | https://openalex.org/I913481162 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | acceptedVersion |
| locations[2].raw_type | Article/Letter to editor |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | True |
| locations[2].is_published | False |
| locations[2].raw_source_name | Altex 39 (2022) 2 |
| locations[2].landing_page_url | https://research.wur.nl/en/publications/predictive-performance-of-next-generation-human-physiologically-b |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5084547667 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2056-4634 |
| authorships[0].author.display_name | Ans Punt |
| authorships[0].countries | NL |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I913481162 |
| authorships[0].affiliations[0].raw_affiliation_string | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[0].institutions[0].id | https://openalex.org/I913481162 |
| authorships[0].institutions[0].ror | https://ror.org/04qw24q55 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I913481162 |
| authorships[0].institutions[0].country_code | NL |
| authorships[0].institutions[0].display_name | Wageningen University & Research |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ans Punt |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[1].author.id | https://openalex.org/A5063685802 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0517-2288 |
| authorships[1].author.display_name | Jochem Louisse |
| authorships[1].countries | NL |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I913481162 |
| authorships[1].affiliations[0].raw_affiliation_string | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[1].institutions[0].id | https://openalex.org/I913481162 |
| authorships[1].institutions[0].ror | https://ror.org/04qw24q55 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I913481162 |
| authorships[1].institutions[0].country_code | NL |
| authorships[1].institutions[0].display_name | Wageningen University & Research |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jochem Louisse |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[2].author.id | https://openalex.org/A5009684017 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6673-8644 |
| authorships[2].author.display_name | Karsten Beekmann |
| authorships[2].countries | NL |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I913481162 |
| authorships[2].affiliations[0].raw_affiliation_string | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[2].institutions[0].id | https://openalex.org/I913481162 |
| authorships[2].institutions[0].ror | https://ror.org/04qw24q55 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I913481162 |
| authorships[2].institutions[0].country_code | NL |
| authorships[2].institutions[0].display_name | Wageningen University & Research |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Karsten Beekmann |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[3].author.id | https://openalex.org/A5052147035 |
| authorships[3].author.orcid | https://orcid.org/0009-0006-6879-5243 |
| authorships[3].author.display_name | Nicole Pinckaers |
| authorships[3].countries | NL |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I913481162 |
| authorships[3].affiliations[0].raw_affiliation_string | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[3].institutions[0].id | https://openalex.org/I913481162 |
| authorships[3].institutions[0].ror | https://ror.org/04qw24q55 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I913481162 |
| authorships[3].institutions[0].country_code | NL |
| authorships[3].institutions[0].display_name | Wageningen University & Research |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Nicole Pinckaers |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Wageningen Food Safety Research, Wageningen, The Netherlands. |
| authorships[4].author.id | https://openalex.org/A5041724973 |
| authorships[4].author.orcid | https://orcid.org/0009-0001-7592-5580 |
| authorships[4].author.display_name | Eric Fabian |
| authorships[4].countries | DE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210107087 |
| authorships[4].affiliations[0].raw_affiliation_string | BASF SE, Experimental Toxicology and Ecology, Ludwigshafen, Germany. |
| authorships[4].institutions[0].id | https://openalex.org/I4210107087 |
| authorships[4].institutions[0].ror | https://ror.org/01q8f6705 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210107087 |
| authorships[4].institutions[0].country_code | DE |
| authorships[4].institutions[0].display_name | BASF (Germany) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Eric Fabian |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | BASF SE, Experimental Toxicology and Ecology, Ludwigshafen, Germany. |
| authorships[5].author.id | https://openalex.org/A5064793035 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-4264-9217 |
| authorships[5].author.display_name | Bennard van Ravenzwaay |
| authorships[5].countries | DE |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210107087 |
| authorships[5].affiliations[0].raw_affiliation_string | BASF SE, Experimental Toxicology and Ecology, Ludwigshafen, Germany. |
| authorships[5].institutions[0].id | https://openalex.org/I4210107087 |
| authorships[5].institutions[0].ror | https://ror.org/01q8f6705 |
| authorships[5].institutions[0].type | company |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210107087 |
| authorships[5].institutions[0].country_code | DE |
| authorships[5].institutions[0].display_name | BASF (Germany) |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Bennard Van Ravenzwaay |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | BASF SE, Experimental Toxicology and Ecology, Ludwigshafen, Germany. |
| authorships[6].author.id | https://openalex.org/A5030567966 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-1353-9032 |
| authorships[6].author.display_name | Paul L. Carmichael |
| authorships[6].countries | GB |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I1342131907 |
| authorships[6].affiliations[0].raw_affiliation_string | SEAC, Unilever, Colworth Science Park, Sharnbrook, Bedford, UK. |
| authorships[6].institutions[0].id | https://openalex.org/I1342131907 |
| authorships[6].institutions[0].ror | https://ror.org/05n8ah907 |
| authorships[6].institutions[0].type | company |
| authorships[6].institutions[0].lineage | https://openalex.org/I1342131907 |
| authorships[6].institutions[0].country_code | GB |
| authorships[6].institutions[0].display_name | Unilever (United Kingdom) |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Paul L Carmichael |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | SEAC, Unilever, Colworth Science Park, Sharnbrook, Bedford, UK. |
| authorships[7].author.id | https://openalex.org/A5077095674 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-9345-9897 |
| authorships[7].author.display_name | Ian Sorrell |
| authorships[7].countries | GB |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I1342131907 |
| authorships[7].affiliations[0].raw_affiliation_string | SEAC, Unilever, Colworth Science Park, Sharnbrook, Bedford, UK. |
| authorships[7].institutions[0].id | https://openalex.org/I1342131907 |
| authorships[7].institutions[0].ror | https://ror.org/05n8ah907 |
| authorships[7].institutions[0].type | company |
| authorships[7].institutions[0].lineage | https://openalex.org/I1342131907 |
| authorships[7].institutions[0].country_code | GB |
| authorships[7].institutions[0].display_name | Unilever (United Kingdom) |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Ian Sorrell |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | SEAC, Unilever, Colworth Science Park, Sharnbrook, Bedford, UK. |
| authorships[8].author.id | https://openalex.org/A5043181187 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-7551-8141 |
| authorships[8].author.display_name | Thomas E. Moxon |
| authorships[8].countries | GB |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I1342131907 |
| authorships[8].affiliations[0].raw_affiliation_string | SEAC, Unilever, Colworth Science Park, Sharnbrook, Bedford, UK. |
| authorships[8].institutions[0].id | https://openalex.org/I1342131907 |
| authorships[8].institutions[0].ror | https://ror.org/05n8ah907 |
| authorships[8].institutions[0].type | company |
| authorships[8].institutions[0].lineage | https://openalex.org/I1342131907 |
| authorships[8].institutions[0].country_code | GB |
| authorships[8].institutions[0].display_name | Unilever (United Kingdom) |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Thomas E Moxon |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | SEAC, Unilever, Colworth Science Park, Sharnbrook, Bedford, UK. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.14573/altex.2108301 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-01-26T00:00:00 |
| display_name | Predictive performance of next generation human physiologically based kinetic (PBK) model predictions based on in vitro and in silico input data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10211 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9952999949455261 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1703 |
| primary_topic.subfield.display_name | Computational Theory and Mathematics |
| primary_topic.display_name | Computational Drug Discovery Methods |
| related_works | https://openalex.org/W2333605542, https://openalex.org/W2966688321, https://openalex.org/W4321334971, https://openalex.org/W2056555318, https://openalex.org/W2138350991, https://openalex.org/W4308679052, https://openalex.org/W4310707532, https://openalex.org/W2086768837, https://openalex.org/W2599017508, https://openalex.org/W2875713025 |
| cited_by_count | 20 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 4 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 6 |
| locations_count | 3 |
| best_oa_location.id | doi:10.14573/altex.2108301 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210171861 |
| best_oa_location.source.issn | 1868-596X, 1868-8551 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1868-596X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | ALTEX |
| best_oa_location.source.host_organization | https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_name | Springer Nature |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | ALTEX |
| best_oa_location.landing_page_url | https://doi.org/10.14573/altex.2108301 |
| primary_location.id | doi:10.14573/altex.2108301 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210171861 |
| primary_location.source.issn | 1868-596X, 1868-8551 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1868-596X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | ALTEX |
| primary_location.source.host_organization | https://openalex.org/P4310319965 |
| primary_location.source.host_organization_name | Springer Nature |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | ALTEX |
| primary_location.landing_page_url | https://doi.org/10.14573/altex.2108301 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 183 |
| abstract_inverted_index.a | 13, 47 |
| abstract_inverted_index.(> | 178 |
| abstract_inverted_index.10 | 170 |
| abstract_inverted_index.19 | 159 |
| abstract_inverted_index.34 | 140 |
| abstract_inverted_index.44 | 56, 144 |
| abstract_inverted_index.by | 58 |
| abstract_inverted_index.in | 23, 26, 63, 66, 81, 96, 130, 197, 208, 233, 236 |
| abstract_inverted_index.of | 2, 12, 33, 51, 62, 109, 121, 139, 142, 150, 158, 169, 185, 228, 242 |
| abstract_inverted_index.on | 22, 41, 95, 232, 246 |
| abstract_inverted_index.or | 100 |
| abstract_inverted_index.to | 7, 79, 201 |
| abstract_inverted_index.For | 44 |
| abstract_inverted_index.PBK | 189, 194, 211, 229 |
| abstract_inverted_index.S9) | 102 |
| abstract_inverted_index.The | 0, 165 |
| abstract_inverted_index.and | 25, 30, 65, 72, 111, 118, 123, 154, 235, 239 |
| abstract_inverted_index.did | 180 |
| abstract_inverted_index.for | 38, 55, 69, 113, 125 |
| abstract_inverted_index.not | 181, 206 |
| abstract_inverted_index.out | 141 |
| abstract_inverted_index.the | 3, 9, 31, 89, 107, 119, 127, 135, 143, 151, 155, 186, 209, 216, 219, 225, 240, 247 |
| abstract_inverted_index.was | 6, 49, 92, 199 |
| abstract_inverted_index.Best | 84 |
| abstract_inverted_index.Cmax | 53, 75, 137, 156, 167 |
| abstract_inverted_index.With | 132 |
| abstract_inverted_index.data | 29 |
| abstract_inverted_index.goal | 1 |
| abstract_inverted_index.into | 224 |
| abstract_inverted_index.made | 200 |
| abstract_inverted_index.more | 173 |
| abstract_inverted_index.than | 174 |
| abstract_inverted_index.that | 213 |
| abstract_inverted_index.this | 45 |
| abstract_inverted_index.vivo | 82 |
| abstract_inverted_index.were | 77, 86, 146, 161, 172 |
| abstract_inverted_index.when | 88 |
| abstract_inverted_index.with | 192 |
| abstract_inverted_index.(PBK) | 19 |
| abstract_inverted_index.Cmax, | 153 |
| abstract_inverted_index.based | 17, 21, 94, 231 |
| abstract_inverted_index.data. | 83 |
| abstract_inverted_index.human | 15 |
| abstract_inverted_index.input | 28, 36, 238, 244 |
| abstract_inverted_index.liver | 101 |
| abstract_inverted_index.might | 214 |
| abstract_inverted_index.model | 20, 190, 212, 248 |
| abstract_inverted_index.study | 5 |
| abstract_inverted_index.these | 73, 133 |
| abstract_inverted_index.those | 42 |
| abstract_inverted_index.using | 34 |
| abstract_inverted_index.vitro | 24, 64, 97, 234 |
| abstract_inverted_index.(i.e., | 98 |
| abstract_inverted_index.38,772 | 52 |
| abstract_inverted_index.5-fold | 149, 175 |
| abstract_inverted_index.Lobell | 122 |
| abstract_inverted_index.assess | 8 |
| abstract_inverted_index.effect | 32 |
| abstract_inverted_index.median | 136, 166 |
| abstract_inverted_index.method | 108, 120 |
| abstract_inverted_index.models | 195, 230 |
| abstract_inverted_index.occur. | 182 |
| abstract_inverted_index.silico | 27, 67, 237 |
| abstract_inverted_index.values | 76, 138, 157, 168 |
| abstract_inverted_index.within | 148, 163 |
| abstract_inverted_index.2-fold. | 164 |
| abstract_inverted_index.5-fold) | 179 |
| abstract_inverted_index.Rodgers | 110 |
| abstract_inverted_index.Rowland | 112 |
| abstract_inverted_index.created | 50 |
| abstract_inverted_index.crucial | 222 |
| abstract_inverted_index.current | 187 |
| abstract_inverted_index.dataset | 48 |
| abstract_inverted_index.explain | 215 |
| abstract_inverted_index.generic | 14, 188, 210 |
| abstract_inverted_index.hepatic | 90 |
| abstract_inverted_index.kinetic | 18, 204 |
| abstract_inverted_index.plasma. | 131 |
| abstract_inverted_index.present | 4 |
| abstract_inverted_index.provide | 221 |
| abstract_inverted_index.results | 85, 220 |
| abstract_inverted_index.unbound | 129 |
| abstract_inverted_index.values, | 106 |
| abstract_inverted_index.Overall, | 218 |
| abstract_inverted_index.achieved | 87 |
| abstract_inverted_index.applying | 59 |
| abstract_inverted_index.chemical | 39, 70 |
| abstract_inverted_index.compared | 78 |
| abstract_inverted_index.fraction | 128 |
| abstract_inverted_index.identify | 202 |
| abstract_inverted_index.included | 207 |
| abstract_inverted_index.insights | 223 |
| abstract_inverted_index.measured | 103 |
| abstract_inverted_index.observed | 152 |
| abstract_inverted_index.possible | 203 |
| abstract_inverted_index.purpose, | 46 |
| abstract_inverted_index.reported | 80 |
| abstract_inverted_index.Sivarajah | 124 |
| abstract_inverted_index.available | 196 |
| abstract_inverted_index.clearance | 91, 105 |
| abstract_inverted_index.compounds | 57, 145, 160, 171 |
| abstract_inverted_index.different | 35, 60, 243 |
| abstract_inverted_index.influence | 241 |
| abstract_inverted_index.intrinsic | 104 |
| abstract_inverted_index.partition | 116 |
| abstract_inverted_index.predicted | 74, 147, 162 |
| abstract_inverted_index.processes | 205 |
| abstract_inverted_index.structure | 191 |
| abstract_inverted_index.approaches | 37, 68, 245 |
| abstract_inverted_index.comparison | 184 |
| abstract_inverted_index.literature | 198 |
| abstract_inverted_index.predictive | 10, 226 |
| abstract_inverted_index.calculating | 114, 126 |
| abstract_inverted_index.hepatocytes | 99 |
| abstract_inverted_index.parameters, | 134 |
| abstract_inverted_index.performance | 11, 227 |
| abstract_inverted_index.predictions | 54 |
| abstract_inverted_index.combinations | 61 |
| abstract_inverted_index.predictions. | 43, 249 |
| abstract_inverted_index.coefficients, | 117 |
| abstract_inverted_index.parameterized | 93 |
| abstract_inverted_index.tissue:plasma | 115 |
| abstract_inverted_index.overestimated. | 176 |
| abstract_inverted_index.physiologically | 16 |
| abstract_inverted_index.Underestimations | 177 |
| abstract_inverted_index.overestimations. | 217 |
| abstract_inverted_index.parameterization | 40 |
| abstract_inverted_index.chemical-specific | 193 |
| abstract_inverted_index.parameterization, | 71 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.92802963 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |