Kuentz, Martin

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Martin
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Kuentz, Martin

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Lipid based formulations as supersaturating oral delivery systems. From current to future industrial applications

2023-10-01, Holm, René, Kuentz, Martin, Ilie-Spiridon, Alexandra-Roxana, Griffin, Brendan T.

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Using a laser monitoring technique for dissolution and thermodynamic study of celecoxib in 2-propanol and propylene glycol mixtures

2023, Jouyban-Gharamaleki, Vahid, Martinez, Fleming, Kuentz, Martin, Rahimpour, Elaheh, Jouyban, Abolghasem

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Study of disordered mesoporous silica regarding intrinsic compound affinity to the carrier and drug-accessible surface area

2023, Niederquell, Andreas, Vraníková, Barbora, Kuentz, Martin

There is increasing research interest in using mesoporous silica for the delivery of poorly water-soluble drugs that are stabilized in a noncrystalline form. Most research has been done on ordered silica, whereas far fewer studies have been published on using nonordered mesoporous silica, and little is known about intrinsic drug affinity to the silica surface. The present mechanistic study uses inverse gas chromatography (IGC) to analyze the surface energies of three different commercially available disordered mesoporous silica grades in the gas phase. Using the more drug-like probe molecule octane instead of nitrogen, the concept of a “drug-accessible surface area” is hereby introduced, and the effect on drug monolayer capacity is addressed. In addition, enthalpic interactions of molecules with the silica surface were calculated based on molecular mechanics, and entropic energy contributions of volatiles were estimated considering molecular flexibility. These free energy contributions were used in a regression model, giving a successful comparison with experimental desorption energies from IGC. It is proposed that a simplified model for drugs based only on the enthalpic interactions can provide an affinity ranking to the silica surface. Following this preformulation research on mesoporous silica, future studies may harness the presented concepts to guide formulation scientists. © 2023 American Chemical Society.

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Exploring the cocrystal landscape of posaconazole by combining high-throughput screening experimentation with computational chemistry

2022-12, Guidetti, Matteo, Hilfiker, Rolf, Kuentz, Martin, Bauer-Brandl, Annette, Blatter, Fritz

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Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products. An InPharma commentary

2023-09-01, 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.

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Corrigendum to “Powder cohesion and energy to break an avalanche. Can we address surface heterogeneity?” [Int. J. Pharm. 626 (2022) 122198]

2023, Brokešová, Jana, Niederquell, Andreas, Kuentz, Martin, Zámostný, Petr, Vraníková, Barbora, Šklubalová, Zdenka

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Comparative drug solubility studies using shake-flask versus a laser-based robotic method

2023, Rahimpour, Elaheh, Moradi, Milad, Sheikhi-Sovari, Atefeh, Rezaei, Homa, Rezaei, Hadis, Jouyban-Gharamaleki, Vahid, Kuentz, Martin, Jouyban, Abolghasem

Drug solubility is of central importance to the pharmaceutical sciences, but reported values often show discrepancies. Various factors have been discussed in the literature to account for such differences, but the influence of manual testing in comparison to a robotic system has not been studied adequately before. In this study, four expert researchers were asked to measure the solubility of four drugs with various solubility behaviors (i.e., paracetamol, mesalazine, lamotrigine, and ketoconazole) in the same laboratory with the same instruments, method, and material sources and repeated their measurements after a time interval. In addition, the same solubility data were determined using an automated laser-based setup. The results suggest that manual testing leads to a handling influence on measured solubility values, and the results were discussed in more detail as compared to the automated laser-based system. Within the framework of unavoidable uncertainties of solubility testing, it is a possibility to combine minimal experimental testing that is preferably automated with mathematical modeling. That is a practical suggestion to support future pharmaceutical development in a more efficient way. © 2023, The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.

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Physiological buffer effects in drug supersaturation - a mechanistic study of hydroxypropyl cellulose as precipitation inhibitor

2023, Niederquell, Andreas, Stoyanov, Edmont, Kuentz, Martin

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Advancing algorithmic drug product development. Recommendations for machine learning approaches in drug formulation

2023, Murray, Jack D., Lange, Justus J., Bennett-Lenane, Harriet, Holm, René, Kuentz, Martin, O'Dwyer, Patrick J., Griffin, Brendan T.

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

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Exploring the cocrystal landscape of posaconazole by combining high-throughput screening experimentation with computational chemistry

2022-12-23, Guidetti, Matteo, Hilfiker, Rolf, Kuentz, Martin, Bauer-Brandl, Annette, Blatter, Fritz

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.