Kuentz, Martin

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Kuentz, Martin

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2018 - 201982020 - 202314
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  • Vorschaubild
    Publikation
    Lipid based formulations as supersaturating oral delivery systems. From current to future industrial applications
    (Elsevier, 01.10.2023) Holm, René; Kuentz, Martin; Ilie-Spiridon, Alexandra-Roxana; Griffin, Brendan T. [in: European Journal of Pharmaceutical Sciences]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products. An InPharma commentary
    (Elsevier, 01.09.2023) Reppas, Christos; Kuentz, Martin; Bauer-Brandl, Annette; Carlert, Sara; Dallmann, André; Dietrich, Shirin; Dressman, Jennifer; Ejskjaer, Lotte; Frechen, Sebastian; Guidetti, Matteo; Holm, René; Holzem, Florentin Lukas; Karlsson, Εva; Kostewicz, Edmund; Panbachi, Shaida; Paulus, Felix; Senniksen, Malte Bøgh; Stillhart, Cordula; Turner, David B.; Vertzoni, Maria; Vrenken, Paul; Zöller, Laurin; Griffin, Brendan T.; O'Dwyer, Patrick J. [in: European Journal of Pharmaceutical Sciences]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Physiological buffer effects in drug supersaturation - a mechanistic study of hydroxypropyl cellulose as precipitation inhibitor
    (2023) Niederquell, Andreas; Stoyanov, Edmont; Kuentz, Martin [in: Journal of Pharmaceutical Sciences]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Advancing algorithmic drug product development. Recommendations for machine learning approaches in drug formulation
    (Elsevier, 2023) Murray, Jack D.; Lange, Justus J.; Bennett-Lenane, Harriet; Holm, René; Kuentz, Martin; O'Dwyer, Patrick J.; Griffin, Brendan T. [in: European Journal of Pharmaceutical Sciences]
    Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators. © 2023
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Exploring the cocrystal landscape of posaconazole by combining high-throughput screening experimentation with computational chemistry
    (American Chemical Society, 23.12.2022) Guidetti, Matteo; Hilfiker, Rolf; Kuentz, Martin; Bauer-Brandl, Annette; Blatter, Fritz [in: Crystal Growth & Design]
    The development of multicomponent crystal forms, such as cocrystals, represents a means to enhance the dissolution and absorption properties of poorly water-soluble drug compounds. However, the successful discovery of new pharmaceutical cocrystals remains a time- and resource-consuming process. This study proposes the use of a combined computational-experimental high-throughput approach as a tool to accelerate and improve the efficiency of cocrystal screening exemplified by posaconazole. First, we employed the COSMOquick software to preselect and rank cocrystal candidates (coformers). Second, high-throughput crystallization experiments (HTCS) were conducted on the selected coformers. The HTCS results were successfully reproduced by liquid-assisted grinding and reaction crystallization, ultimately leading to the synthesis of thirteen new posaconazole cocrystals (7 anhydrous, 5 hydrates, and 1 solvate). The posaconazole cocrystals were characterized by PXRD, 1H NMR, Fourier transform-Raman, thermogravimetry–Fourier transform infrared spectroscopy, and differential scanning calorimetry. In addition, the prediction performance of COSMOquick was compared to that of two alternative knowledge-based methods: molecular complementarity (MC) and hydrogen bond propensity (HBP). Although HBP does not perform better than random guessing for this case study, both MC and COSMOquick show good discriminatory ability, suggesting their use as a potential virtual tool to improve cocrystal screening.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Exploring the cocrystal landscape of posaconazole by combining high-throughput screening experimentation with computational chemistry
    (American Chemical Society, 12/2022) Guidetti, Matteo; Hilfiker, Rolf; Kuentz, Martin; Bauer-Brandl, Annette; Blatter, Fritz [in: Crystal Growth & Design]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Mechanistic study of dissolution enhancement by interactive mixtures of chitosan with meloxicam as model
    (Elsevier, 01.02.2022) Kuentz, Martin; Brokesova, Jana; Slamova, Michaela; Zamostny, Petr; Koktan, Jakub; Kreicik, Lukas; Svacinova, Petra; Sklubalova, Zdenka; Vraníková, Barbora [in: European Journal of Pharmaceutical Sciences]
    To enhance dissolution rate of meloxicam (MX), a poorly soluble model drug, a natural polysaccharide excipient chitosan (CH) is employed in this work as a carrier to prepare binary interactive mixtures by either mixing or co-milling techniques. The MX-CH mixtures of three different drug loads were characterized for morphological, granulometric, and thermal properties as well as drug crystallinity. The relative dissolution rate of MX was determined in phosphate buffer of pH 6.8 using the USP-4 apparatus; a significant increase in MX dissolution rate was observed for both mixed and co-milled mixtures comparing to the raw drug. Higher dissolution rate of MX was evidently connected to surface activation by mixing or milling, which was pronounced by the higher specific surface energy as detected by inverse gas chromatography. In addition to the particle size reduction, the carrier effect of the CH was confirmed for co-milling by linear regression between the MX maximum relative dissolution rate and the total surface area of the mixture (R2 = 0.863). No MX amorphization or crystalline structure change were detected. The work of adhesion/cohesion ratio of 0.9 supports the existence of preferential adherence of MX to the coarse particles of CH to form stable interactive mixtures.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations. A pilot study
    (MDPI, 05.09.2021) Bennett-Lenane, Harriett; O'Shea, Joseph; Murray, Jack; Ilie, Alexandra Roxana; Holm, Rene; Kuentz, Martin; Griffin, Brendan [in: Pharmaceutics]
    In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    In Silico, In Vitro, and In Vivo evaluation of precipitation inhibitors in supersaturated lipid-based formulations of venetoclax
    (American Chemical Society, 23.04.2021) Koehl, Niklas; Henze, Laura; Bennett-Lenane, Harriett; Faisal, Waleed; Price, Daniel J.; Holm, Rene; Kuentz, Martin; Griffin, Brendan [in: Molecular Pharmaceutics]
    The concept of using precipitation inhibitors (PIs) to sustain supersaturation is well established for amorphous formulations but less in the case of lipid-based formulations (LBF). This study applied a systematic in silico–in vitro–in vivo approach to assess the merits of incorporating PIs in supersaturated LBFs (sLBF) using the model drug venetoclax. sLBFs containing hydroxypropyl methylcellulose (HPMC), hydroxypropyl methylcellulose acetate succinate (HPMCAS), polyvinylpyrrolidone (PVP), PVP-co-vinyl acetate (PVP/VA), Pluronic F108, and Eudragit EPO were assessed in silico calculating a drug–excipient mixing enthalpy, in vitro using a PI solvent shift test, and finally, bioavailability was assessed in vivo in landrace pigs. The estimation of pure interaction enthalpies of the drug and the excipient was deemed useful in determining the most promising PIs for venetoclax. The sLBF alone (i.e., no PI present) displayed a high initial drug concentration in the aqueous phase during in vitro screening. sLBF with Pluronic F108 displayed the highest venetoclax concentration in the aqueous phase and sLBF with Eudragit EPO the lowest. In vivo, the sLBF alone showed the highest bioavailability of 26.3 ± 14.2%. Interestingly, a trend toward a decreasing bioavailability was observed for sLBF containing PIs, with PVP/VA being significantly lower compared to sLBF alone. In conclusion, the ability of a sLBF to generate supersaturated concentrations of venetoclax in vitro was translated into increased absorption in vivo. While in silico and in vitro PI screening suggested benefits in terms of prolonged supersaturation, the addition of a PI did not increase in vivo bioavailability. The findings of this study are of particular relevance to pre-clinical drug development, where the high in vivo exposure of venetoclax was achieved using a sLBF approach, and despite the perceived risk of drug precipitation from a sLBF, including a PI may not be merited in all cases.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Current challenges and future perspectives in oral absorption research. An opinion of the UNGAP network
    (Elsevier, 04/2021) Kuentz, Martin; Vinarov, Zahari; Bertil, Abrahamsson; Artursson, Per; Batchelor, Hannah; Berben, Philippe; Bernkop-Schnürch, Andreas; Butler, James; Ceulemans, Jens; Davies, Nigel; Dupont, Didier; Eide Flaten, Goril; Fotaki, Nikoleta; Jannin, Vincent; Keemink, Janneke; Kesisoglou, Filippos; Koziolek, Mirko; Augustijns, Patrick; Griffin, Brendan [in: Advanced Drug Delivery Reviews]
    Although oral drug delivery is the preferred administration route and has been used for centuries, modern drug discovery and development pipelines challenge conventional formulation approaches and highlight the insufficient mechanistic understanding of processes critical to oral drug absorption. This review presents the opinion of UNGAP scientists on four key themes across the oral absorption landscape: (1) specific patient populations, (2) regional differences in the gastrointestinal tract, (3) advanced formulations and (4) food-drug interactions. The differences of oral absorption in pediatric and geriatric populations, the specific issues in colonic absorption, the formulation approaches for poorly water-soluble (small molecules) and poorly permeable (peptides, RNA etc.) drugs, as well as the vast realm of food effects, are some of the topics discussed in detail. The identified controversies and gaps in the current understanding of gastrointestinal absorption-related processes are used to create a roadmap for the future of oral drug absorption research.
    01A - Beitrag in wissenschaftlicher Zeitschrift