Biology subjects

Kloprogge, F.

Publications and source records attributed to Kloprogge, F..

4 recordsLinked to original sources

bioRxiv2024

Quinoline synergy and reduced use: a study of pharmacodynamic interactions

SynopsisO_ST_ABSBackgroundC_ST_ABSMeropenem, gentamicin and ciprofloxacin have been used as empiric broad-spectrum combination therapy in different combinations. Recent restrictions on the use of quinolones jeopardises the rational of administering this combination to increase the spectrum of coverage for this particular case. A mechanistic understanding of pharmacodynamic interaction for these combinations is lacking but can provide insight in the necessity of using the different moieties. ObjectivesTo study pharmacodynamic drug-drug interaction between meropenem, gentamicin and ciprofloxacin against Escherichia coli. MethodsStatic time kill curve experiments were conducted with Escherichia coli (NCTC(R) 12241) at 0.25 - 16 x MIC for a duration of 24 hours with samples being collected at 0, 2, 4, 6, 8, and 24 hour. Meropenem, gentamicin and ciprofloxacin were tested alone, in two- and three-way combinations. Bacterial load time series data were enumerated on Meuller Hinton plates and Colony Forming Unit data was modelled using nonlinear mixed-effects models in nlmixr. ResultsMeropenem, gentamicin and ciprofloxacin two- and three-way combinations prevented regrowth, but did not when these moieties were studied alone. Gentamicin and meropenem were synergistic by decreasing ciprofloxacin IC50 and the combination effects of meropenem and gentamicin and the addition of meropenem on top of a gentamicin and ciprofloxacin combination were indifferent. ConclusionsOur findings emphasize the added value of a quinolone in the drug combination. In light of the recent move towards reduced use of quinolones, a quinolone free combination still prevented regrowth, it just did not display further synergy on IC50 and was indifferent in initial killing.

pharmacology and toxicology↗

bioRxiv2024

The impact of physiological state and environmental stress on bacterial load estimation methodologies for Mycobacterium tuberculosis

SynopsisO_ST_ABSBackgroundC_ST_ABSSolid and liquid medium cultures from patient samples recover different proportions of a heterogenous bacterial community over the duration of treatment. In vitro experiments were designed to study the population composition at early-logarithmic and stationary phases of growth as well as under drug pressure. ObjectivesTo derive a relationship between methodologies for bacterial load determination and assess the effect of the growth phase of the parent culture and its exposure to stress on the results. MethodsMycobacterium tuberculosis H37Rv was grown with and without drug (isoniazid or rifampicin) and sampled on day 0, 3, 11 and 21 of growth in broth culture. The bacterial load was estimated by colony counts and the BD BACTEC MGIT automated mycobacterial detection system. Linear and nonlinear mixed-effects models were used to describe the relationship between time-to-positivity (TTP) and time-to-growth (TTG) vs colony forming units (CFU), and growth units (GU) vs time. ResultsFor samples with the same CFU, drug-treated and stationary phase cells had a shorter TTP than the drug-free control and early-logarithmic phase cells respectively. Similarly, stationary phase samples reached higher GUs and had shorter time to start growing than early-log phase ones. ConclusionsThe growth phase affects the relationship between CFU-TTP/TTG and previous exposure to drugs affects only the relationship between CFU-TTP. This suggests that there is a population of bacterial cells that can be differentially recovered in liquid medium giving us an insight into the physiological states of the original culture which aids in the interpretation of clinical trial outputs.

microbiology↗

bioRxiv2024

Named Entity Recognition of Pharmacokinetic parameters in the scientific literature

The development of accurate predictions for a new drugs absorption, distribution, metabolism, and excretion profiles in the early stages of drug development is crucial due to high candidate failure rates. The absence of comprehensive, standardised, and updated pharmacokinetic (PK) repositories limits pre-clinical predictions and often requires searching through the scientific literature for PK parameter estimates from similar compounds. While text mining offers promising advancements in automatic PK parameter extraction, accurate Named Entity Recognition (NER) of PK terms remains a bottleneck due to limited resources. This work addresses this gap by introducing novel corpora and language models specifically designed for effective NER of PK parameters. Leveraging active learning approaches, we developed an annotated corpus containing over 4,000 entity mentions found across the PK literature on PubMed. To identify the most effective model for PK NER, we fine-tuned and evaluated different NER architectures on our corpus. Fine-tuning BioBERT exhibited the best results, achieving a strict F1 score of 90.37% in recognising PK parameter mentions, significantly outperforming heuristic approaches and models trained on existing corpora. To accelerate the development of end-to-end PK information extraction pipelines and improve pre-clinical PK predictions, the PK NER models and the labelled corpus were released open source at https://github.com/PKPDAI/PKNER.

pharmacology and toxicology↗

bioRxiv2022

Understanding emergence of antimycobacterial dose dependent resistance.

Concentration dependency of phenotypic and genotypic isoniazid-rifampicin resistance emergence was investigated to obtain a mechanistic understanding on how anti-mycobacterial drugs facilitate the emergence of bacterial populations that survive throughout treatment. Using static kill curve experiments, observing two evolution cycles, it was demonstrated that rifampicin resistance was the result of non-specific mechanisms and not associated with accumulation of drug resistance encoding SNPs. Whereas, part of isoniazid resistance could be accounted for by accumulation of specific SNPs, which was concentration dependent. Using a Hollow Fibre Infection Model it was demonstrated that emergence of genotypic resistance only occurs when antibiotic levels fall below MIC although MICs are typically maintained following clinical dosing provided that adherence to the regimen is good. This study showed that disentangling and quantifying concentration dependent emergence of resistance provides improved rational for drug and dose selection although further work on understanding underlying mechanisms is needed to improve the drug development pipeline. One Sentence SummaryDisentangling and quantifying concentration dependent emergence of resistance will contribute to better informed drug and dose selection for anti-mycobacterial combination therapy.

pharmacology and toxicology↗