M. O. Karlsson

758 total citations
17 papers, 623 citations indexed

About

M. O. Karlsson is a scholar working on Oncology, Pharmacology and Statistics and Probability. According to data from OpenAlex, M. O. Karlsson has authored 17 papers receiving a total of 623 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Oncology, 6 papers in Pharmacology and 4 papers in Statistics and Probability. Recurrent topics in M. O. Karlsson's work include Cancer Treatment and Pharmacology (7 papers), Pharmacogenetics and Drug Metabolism (6 papers) and Statistical Methods in Clinical Trials (4 papers). M. O. Karlsson is often cited by papers focused on Cancer Treatment and Pharmacology (7 papers), Pharmacogenetics and Drug Metabolism (6 papers) and Statistical Methods in Clinical Trials (4 papers). M. O. Karlsson collaborates with scholars based in Sweden, Netherlands and United States. M. O. Karlsson's co-authors include E. Niclas Jonsson, Janet R. Wade, Curtis G. Wiltse, Agneta Freijs, Marie Sandström, Alex Sparreboom, Lena E. Friberg, Arne Melander, Anders Jönsson and Tony Rydberg and has published in prestigious journals such as Journal of Clinical Oncology, Journal of Pharmacology and Experimental Therapeutics and European Journal of Cancer.

In The Last Decade

M. O. Karlsson

17 papers receiving 602 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
M. O. Karlsson Sweden	11	272	119	108	84	82	17	623
Haiqing Dai United States	11	343 1.3×	48 0.4×	129 1.2×	55 0.7×	135 1.6×	24	714
Sandra R. B. Allerheiligen United States	11	215 0.8×	71 0.6×	186 1.7×	77 0.9×	42 0.5×	16	586
Diane D. Wang United States	14	372 1.4×	102 0.9×	191 1.8×	40 0.5×	90 1.1×	29	825
William J. Jusko United States	12	191 0.7×	137 1.2×	325 3.0×	49 0.6×	85 1.0×	19	921
Milly E. de Jonge Netherlands	14	282 1.0×	150 1.3×	219 2.0×	21 0.3×	120 1.5×	17	867
Madelé van Dyk Australia	15	197 0.7×	139 1.2×	182 1.7×	45 0.5×	55 0.7×	35	682
B Booth United States	5	173 0.6×	171 1.4×	88 0.8×	79 0.9×	80 1.0×	17	535
Enaksha Wickremsinhe United States	19	226 0.8×	68 0.6×	310 2.9×	31 0.4×	60 0.7×	43	858
Hugo Maas Germany	12	244 0.9×	88 0.7×	161 1.5×	77 0.9×	30 0.4×	22	1.7k
Donald Heald United States	20	351 1.3×	102 0.9×	252 2.3×	17 0.2×	100 1.2×	36	917

Countries citing papers authored by M. O. Karlsson

Since Specialization

Citations

This map shows the geographic impact of M. O. Karlsson's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. O. Karlsson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. O. Karlsson more than expected).

Fields of papers citing papers by M. O. Karlsson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. O. Karlsson. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. O. Karlsson. The network helps show where M. O. Karlsson may publish in the future.

Co-authorship network of co-authors of M. O. Karlsson

This figure shows the co-authorship network connecting the top 25 collaborators of M. O. Karlsson. A scholar is included among the top collaborators of M. O. Karlsson based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. O. Karlsson. M. O. Karlsson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Jönsson, Siv, et al.. (2019). Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM. Journal of Pharmacokinetics and Pharmacodynamics. 46(6). 591–604. 6 indexed citations

Karlsson, M. O., et al.. (2011). Influence of CYP2B6 516G>T polymorphism and interoccasion variability (IOV) on the population pharmacokinetics of efavirenz in HIV-infected South African children. European Journal of Clinical Pharmacology. 68(4). 339–347. 22 indexed citations

Friberg, Lena E., et al.. (2010). Transforming parts of a differential equations system to difference equations as a method for run-time savings in NONMEM. Journal of Pharmacokinetics and Pharmacodynamics. 37(5). 493–506. 1 indexed citations

Sandanaraj, Edwin, Radojka M. Savić, Sean Lal, et al.. (2008). Population pharmacokinetics of doxorubicin and doxorubicinol in Asian breast cancer patients. Journal of Clinical Oncology. 26(15_suppl). 13501–13501. 1 indexed citations

Rudek, Michelle A., et al.. (2006). PI-51Population pharmacokinetics of COL-3, a matrix metalloproteinase inhibitor, in patients with refractory metastatic cancer. Clinical Pharmacology & Therapeutics. 79(2). P20–P20. 1 indexed citations

Sparreboom, Alex, Sharon Marsh, Anja Henningsson, et al.. (2005). Effect of genetic variants in CYP2C8, CYP3A4, CYP3A5, and ABCB1 on paclitaxel pharmacokinetics. Journal of Clinical Oncology. 23(16_suppl). 2019–2019. 1 indexed citations

Henningsson, Anja, Alex Sparreboom, Marie Sandström, et al.. (2003). Population pharmacokinetic modelling of unbound and total plasma concentrations of paclitaxel in cancer patients. European Journal of Cancer. 39(8). 1105–1114. 65 indexed citations

Lindell, Michael K., M. O. Karlsson, Hans Lennernäs, Lars Påhlman, & M. Lang. (2003). Variable expression of CYP and Pgp genes in the human small intestine. European Journal of Clinical Investigation. 33(6). 493–499. 64 indexed citations

Hassan, Saadia, et al.. (2001). Model for Time Dependency of Cytotoxic Effect of CHS 828 in Vitro Suggests Two Different Mechanisms of Action. Journal of Pharmacology and Experimental Therapeutics. 299(3). 1140–1147. 23 indexed citations

10.

Zuylen, Lia van, M. O. Karlsson, Jaap Verweij, et al.. (2001). Pharmacokinetic modeling of paclitaxel encapsulation in Cremophor EL micelles. Cancer Chemotherapy and Pharmacology. 47(4). 309–318. 109 indexed citations

11.

Friberg, Lena E., Agneta Freijs, Marie Sandström, & M. O. Karlsson. (2000). Semiphysiological Model for the Time Course of Leukocytes after Varying Schedules of 5-Fluorouracil in Rats. Journal of Pharmacology and Experimental Therapeutics. 295(2). 734–740. 63 indexed citations

12.

Jonsson, E. Niclas, M. O. Karlsson, & Janet R. Wade. (2000). Nonlinearity detection: Advantages of nonlinear mixed-effects modeling. PubMed. 2(3). 114–123. 39 indexed citations

13.

Wählby, Ulrika, et al.. (2000). Haematological toxicity following different dosing schedules of 5-fluorouracil and epirubicin in rats.. PubMed. 20(3A). 1519–25. 7 indexed citations

14.

Karlsson, M. O., E. Niclas Jonsson, Curtis G. Wiltse, & Janet R. Wade. (1998). Assumption Testing in Population Pharmacokinetic Models: Illustrated with an Analysis of Moxonidine Data from Congestive Heart Failure Patients. Journal of Pharmacokinetics and Biopharmaceutics. 26(2). 207–246. 124 indexed citations

15.

Rydberg, Tony, Anders Jönsson, M. O. Karlsson, & Arne Melander. (1997). Concentration-effect relations of glibenclamide and its active metabolites in man: modelling of Pharmacokinetics and Pharmacodynamics. British Journal of Clinical Pharmacology. 43(4). 373–381. 58 indexed citations

16.

Karlsson, M. O., et al.. (1991). Population Pharmacokinetics of Rectal Theophylline in Neonates. Therapeutic Drug Monitoring. 13(3). 195–200. 14 indexed citations

17.

Hanhijärvi, H, Inkeri Elomaa, M. O. Karlsson, & L. Laurén. (1989). Pharmacokinetics of disodium clodronate after daily intravenous infusions during five consecutive days.. PubMed. 27(12). 602–6. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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