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1. Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project

Author

Stenton, Sarah L., O’Leary, Melanie C., Lemire, Gabrielle, VanNoy, Grace E., DiTroia, Stephanie, Ganesh, Vijay S., Groopman, Emily, O’Heir, Emily, Mangilog, Brian, Osei-Owusu, Ikeoluwa, Pais, Lynn S., Serrano, Jillian, Singer-Berk, Moriel, Weisburd, Ben, Wilson, Michael W., Austin-Tse, Christina, Abdelhakim, Marwa, Althagafi, Azza, Babbi, Giulia, Bellazzi, Riccardo, Bovo, Samuele, Carta, Maria Giulia, Casadio, Rita, Coenen, Pieter-Jan, De Paoli, Federica, Floris, Matteo, Gajapathy, Manavalan, Hoehndorf, Robert, Jacobsen, Julius O. B., Joseph, Thomas, Kamandula, Akash, Katsonis, Panagiotis, Kint, Cyrielle, Lichtarge, Olivier, Limongelli, Ivan, Lu, Yulan, Magni, Paolo, Mamidi, Tarun Karthik Kumar, Martelli, Pier Luigi, Mulargia, Marta, Nicora, Giovanna, Nykamp, Keith, Pejaver, Vikas, Peng, Yisu, Pham, Thi Hong Cam, Podda, Maurizio S., Rao, Aditya, Rizzo, Ettore, Saipradeep, Vangala G., Savojardo, Castrense, Schols, Peter, Shen, Yang, Sivadasan, Naveen, Smedley, Damian, Soru, Dorian, Srinivasan, Rajgopal, Sun, Yuanfei, Sunderam, Uma, Tan, Wuwei, Tiwari, Naina, Wang, Xiao, Wang, Yaqiong, Williams, Amanda, Worthey, Elizabeth A., Yin, Rujie, You, Yuning, Zeiberg, Daniel, Zucca, Susanna, Bakolitsa, Constantina, Brenner, Steven E., Fullerton, Stephanie M., Radivojac, Predrag, Rehm, Heidi L., and O’Donnell-Luria, Anne

Published
2024
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15. Model‐Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing.

Author

Tosca, Elena Maria, De Carlo, Alessandro, Ronchi, Davide, and Magni, Paolo

Subjects
REINFORCEMENT learning, ESSENTIAL drugs, WARFARIN, READING interests, ANTICOAGULANTS
Abstract

Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize risks in each patient, is essential for drugs with a narrow therapeutic window and severe adverse effects. Adaptive dosing strategies extend the precision dosing concept to time‐varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics the sequential decision‐making process where clinicians adapt dose administration based on patient response and evolution monitoring. This paper aims to investigate the potentiality of coupling RL with population PK/PD models to develop precision dosing algorithms, reviewing the most relevant works in the field. Case studies in which PK/PD models were integrated within RL algorithms as simulation engine to predict consequences of any dosing action have been considered and discussed. They mainly concern propofol‐induced anesthesia, anticoagulant therapy with warfarin and a variety of anticancer treatments differing for administered agents and/or monitored biomarkers. The resulted picture highlights a certain heterogeneity in terms of precision dosing approaches, applied methodologies, and degree of adherence to the clinical domain. In addition, a tutorial on how a precision dosing problem should be formulated in terms of the key elements composing the RL framework (i.e., system state, agent actions and reward function), and on how PK/PD models could enhance RL approaches is proposed for readers interested in delving in this field. Overall, the integration of PK/PD models into a RL‐framework holds great promise for precision dosing, but further investigations and advancements are still needed to address current limitations and extend the applicability of this methodology to drugs requiring adaptive dosing strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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18. A global database of dissolved organic matter (DOM) concentration measurements in coastal waters (CoastDOM v1)

Author

Lønborg, Christian, Carreira, Cátia, Abril, Gwenaël, Agustí, Susana, Amaral, Valentina, Andersson, Agneta, Arístegui, Javier, Bhadury, Punyasloke, Bif, Mariana B., Borges, Alberto V., Bouillon, Steven, Calleja, Maria Ll., Cotovicz Jr., Luiz C., Cozzi, Stefano, Doval, Maryló, Duarte, Carlos M., Eyre, Bradley, Fichot, Cédric G., García-Martín, E. Elena, Garzon-Garcia, Alexandra, Giani, Michele, Gonçalves-Araujo, Rafael, Gruber, Renee, Hansell, Dennis A., Hashihama, Fuminori, He, Ding, Holding, Johnna M., Hunter, William R., Ibánhez, J. Severino P., Ibello, Valeria, Jiang, Shan, Kim, Guebuem, Klun, Katja, Kowalczuk, Piotr, Kubo, Atsushi, Lee, Choon-Weng, Lopes, Cláudia B., Maggioni, Federica, Magni, Paolo, Marrase, Celia, Martin, Patrick, McCallister, S. Leigh, McCallum, Roisin, Medeiros, Patricia M., Morán, Xosé Anxelu G., Muller-Karger, Frank E., Myers-Pigg, Allison, Norli, Marit, Oakes, Joanne M., Osterholz, Helena, Park, Hyukyung, Lund Paulsen, Maria, Rosentreter, Judith A., Ross, Jeff D., Rueda-Roa, Digna, Santinelli, Chiara, Shen, Yuan, Teira, Eva, Tinta, Tinkara, Uher, Guenther, Wakita, Masahide, Ward, Nicholas, Watanabe, Kenta, Xin, Yu, Yamashita, Youhei, Yang, Liyang, Yeo, Jacob, Yuan, Huamao, Zheng, Qiang, Álvarez-Salgado, Xosé Antón, Lønborg, Christian, Carreira, Cátia, Abril, Gwenaël, Agustí, Susana, Amaral, Valentina, Andersson, Agneta, Arístegui, Javier, Bhadury, Punyasloke, Bif, Mariana B., Borges, Alberto V., Bouillon, Steven, Calleja, Maria Ll., Cotovicz Jr., Luiz C., Cozzi, Stefano, Doval, Maryló, Duarte, Carlos M., Eyre, Bradley, Fichot, Cédric G., García-Martín, E. Elena, Garzon-Garcia, Alexandra, Giani, Michele, Gonçalves-Araujo, Rafael, Gruber, Renee, Hansell, Dennis A., Hashihama, Fuminori, He, Ding, Holding, Johnna M., Hunter, William R., Ibánhez, J. Severino P., Ibello, Valeria, Jiang, Shan, Kim, Guebuem, Klun, Katja, Kowalczuk, Piotr, Kubo, Atsushi, Lee, Choon-Weng, Lopes, Cláudia B., Maggioni, Federica, Magni, Paolo, Marrase, Celia, Martin, Patrick, McCallister, S. Leigh, McCallum, Roisin, Medeiros, Patricia M., Morán, Xosé Anxelu G., Muller-Karger, Frank E., Myers-Pigg, Allison, Norli, Marit, Oakes, Joanne M., Osterholz, Helena, Park, Hyukyung, Lund Paulsen, Maria, Rosentreter, Judith A., Ross, Jeff D., Rueda-Roa, Digna, Santinelli, Chiara, Shen, Yuan, Teira, Eva, Tinta, Tinkara, Uher, Guenther, Wakita, Masahide, Ward, Nicholas, Watanabe, Kenta, Xin, Yu, Yamashita, Youhei, Yang, Liyang, Yeo, Jacob, Yuan, Huamao, Zheng, Qiang, and Álvarez-Salgado, Xosé Antón

Abstract

Measurements of dissolved organic carbon (DOC), nitrogen (DON), and phosphorus (DOP) concentrations are used to characterize the dissolved organic matter (DOM) pool and are important components of biogeochemical cycling in the coastal ocean. Here, we present the first edition of a global database (CoastDOM v1; available at https://doi.org/10.1594/PANGAEA.964012, Lønborg et al., 2023) compiling previously published and unpublished measurements of DOC, DON, and DOP in coastal waters. These data are complemented by hydrographic data such as temperature and salinity and, to the extent possible, other biogeochemical variables (e.g. chlorophyll a, inorganic nutrients) and the inorganic carbon system (e.g. dissolved inorganic carbon and total alkalinity). Overall, CoastDOM v1 includes observations of concentrations from all continents. However, most data were collected in the Northern Hemisphere, with a clear gap in DOM measurements from the Southern Hemisphere. The data included were collected from 1978 to 2022 and consist of 62 338 data points for DOC, 20 356 for DON, and 13 533 for DOP. The number of measurements decreases progressively in the sequence DOC > DON > DOP, reflecting both differences in the maturity of the analytical methods and the greater focus on carbon cycling by the aquatic science community. The global database shows that the average DOC concentration in coastal waters (average ± standard deviation (SD): 182±314 µmol C L−1; median: 103 µmol C L−1) is 13-fold higher than the average coastal DON concentration (13.6±30.4 µmol N L−1; median: 8.0 µmol N L−1), which is itself 39-fold higher than the average coastal DOP concentration (0.34±1.11 µmol P L−1; median: 0.18 µmol P L−1). This dataset will be useful for identifying global spatial and temporal patterns in DOM and will help facilitate the reuse of DOC, DON, and DOP data in studies aimed at better characterizing local biogeochemic

Published
2024

19. Metabolic Engineering of Bacillus subtilis for the Production of Poly-γ-Glutamic Acid from Glycerol Feedstock.

Author

Pasotti, Lorenzo, Massaiu, Ilaria, Magni, Paolo, and Calvio, Cinzia

Subjects
BACILLUS subtilis, GLUTAMIC acid, OPERONS, SUSTAINABILITY, MICROBIOLOGICAL synthesis, GLYCERIN, BIOCHEMICAL engineering
Abstract

Poly-γ-glutamic acid (γ-PGA) is an attractive biopolymer for medical, agri-food, and environmental applications. Although microbial synthesis by Bacilli fed on waste streams has been widely adopted, the obtainment of efficient sustainable production processes is still under investigation by bioprocess and metabolic engineering approaches. The abundant glycerol-rich waste generated in the biodiesel industry can be used as a carbon source for γ-PGA production. Here, we studied fermentation performance in different engineered Bacillus subtilis strains in glycerol-based media, considering a swrA+ degU32Hy mutant as the initial producer strain and glucose-based media for comparison. Modifications included engineering the biosynthetic pgs operon regulation (replacing its native promoter with Physpank), precursor accumulation (sucCD or odhAB deletion), and enhanced glutamate racemization (racE overexpression), predicted as crucial reactions by genome-scale model simulations. All interventions increased productivity in glucose-based media, with Physpank-pgs ∆sucCD showing the highest γ-PGA titer (52 g/L). Weaker effects were observed in glycerol-based media: ∆sucCD and Physpank-pgs led to slight improvements under low- and high-glutamate conditions, respectively, reaching ~22 g/L γ-PGA (26% increase). No performance decrease was detected by replacing pure glycerol with crude glycerol waste from a biodiesel plant, and by a 30-fold scale-up. These results may be relevant for improving industrial γ-PGA production efficiency and process sustainability using waste feedstock. The performance differences observed between glucose and glycerol media also motivate additional computational and experimental studies to design metabolically optimized strains. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Reinforcement Learning and PK‐PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma.

Author

De Carlo, Alessandro, Tosca, Elena Maria, Fantozzi, Martina, and Magni, Paolo

Subjects
REINFORCEMENT learning, TRANSITIONAL cell carcinoma, MACHINE learning, ARTIFICIAL intelligence, TREATMENT effectiveness
Abstract

The integration of pharmacokinetic‐pharmacodynamic (PK‐PD) modeling and simulations with artificial intelligence/machine learning algorithms is one of the most attractive areas of the pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, this paper presents and evaluates a new framework embedding PK‐PD models into a reinforcement learning (RL) algorithm, Q‐learning (QL), to personalize pharmacological treatment. Each patient is represented with a set of PK‐PD parameters and has a personal QL agent which optimizes the individual treatment. In the training phase, leveraging PK‐PD simulations, the QL agent assesses different actions, defined consistently with the clinical knowledge to consider only plausible dose‐adjustments, in order to find the optimal rules. The proposed framework was evaluated to optimize the erdafitinib treatment in patients with metastatic urothelial carcinoma. This drug was approved by the US Food and Drug Administration (FDA) with a dose‐adaptive protocol based on monitoring the levels of serum phosphate, which represent a biomarker of both treatment efficacy and toxicity. To evaluate the flexibility of the methodology, a heterogeneous virtual population of 141 patients was generated using an erdafitinib population PK (PopPK)‐PD literature model. For each patient, treatment response was simulated by using both QL‐optimized protocol and the clinical one. QL agents outperform the approved dose‐adaptive rules, increasing more than 10% the efficacy and the safety of treatment at each end point. Results confirm the great potentialities of the integration of PopPK‐PD models and RL algorithms to optimize precision dosing tasks. [ABSTRACT FROM AUTHOR]

Published
2024
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31. A global database of dissolved organic matter (DOM) measurements in coastal waters (CoastDOM v1)

Author

Lønborg, Christian, primary, Carreira, Cátia, additional, Abril, Gwenaël, additional, Agustí, Susana, additional, Amaral, Valentina, additional, Andersson, Agneta, additional, Arístegui, Javier, additional, Bhadury, Punyasloke, additional, Bernardi Bif, Mariana, additional, Borges, Alberto V., additional, Bouillon, Steven, additional, Ll. Calleja, Maria, additional, Cotovicz Jr., Luiz C., additional, Cozzi, Stefano, additional, Doval, Maryló, additional, Duarte, Carlos M., additional, Eyre, Bradley, additional, Fichot, Cédric G., additional, García-Martín, E. Elena, additional, Garzon-Garcia, Alexandra, additional, Giani, Michele, additional, Gonçalves-Araujo, Rafael, additional, Gruber, Renee, additional, A. Hansell, Dennis, additional, Hashihama, Fuminori, additional, He, Ding, additional, Holding, Johnna M., additional, Hunter, William R., additional, Ibánhez, J. Severino P., additional, Ibello, Valeria, additional, Jiang, Shan, additional, Kim, Guebuem, additional, Klun, Katja, additional, Kowalczuk, Piotr, additional, Kubo, Atsushi, additional, Weng Lee, Choon, additional, Lopes, Cláudia B., additional, Maggioni, Federica, additional, Magni, Paolo, additional, Marrase, Celia, additional, Martin, Patrick, additional, McCallister, S. Leigh, additional, McCallum, Roisin, additional, Medeiros, Patricia M., additional, Morán, Xosé Anxelu G., additional, Muller-Karger, Frank, additional, Myers-Pigg, Allison, additional, Norli, Marit, additional, Oakes, Joanne M., additional, Osterholz, Helena, additional, Park, Hyekyung, additional, Lund Paulsen, Maria, additional, Rosentreter, Judith A., additional, Rueda-Roa, Digna, additional, Santinelli, Chiara, additional, Shen, Yuan, additional, Teira, Eva, additional, Tinta, Tinkara, additional, Uher, Guenther, additional, Wakita, Masahide, additional, Ward, Nicholas, additional, Watanabe, Kenta, additional, Xin, Yu, additional, Yamashita, Youhei, additional, Yang, Liyang, additional, Yeo, Jacob, additional, Yuan, Huamao, additional, Zheng, Qiang, additional, and Álvarez-Salgado, Xosé Antón, additional

Published
2023
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41. Insulin-like Growth Factor 1, Growth Hormone, and Anti-Müllerian Hormone Receptors Are Differentially Expressed during GnRH Neuron Development

Author

Paganoni, Alyssa J. J., primary, Cannarella, Rossella, additional, Oleari, Roberto, additional, Amoruso, Federica, additional, Antal, Renata, additional, Ruzza, Marco, additional, Olivieri, Chiara, additional, Condorelli, Rosita A., additional, La Vignera, Sandro, additional, Tolaj, Fationa, additional, Cariboni, Anna, additional, Calogero, Aldo E., additional, and Magni, Paolo, additional

Published
2023
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42. Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project

Author

Stenton, Sarah L., primary, O’Leary, Melanie, additional, Lemire, Gabrielle, additional, VanNoy, Grace E., additional, DiTroia, Stephanie, additional, Ganesh, Vijay S., additional, Groopman, Emily, additional, O’Heir, Emily, additional, Mangilog, Brian, additional, Osei-Owusu, Ikeoluwa, additional, Pais, Lynn S., additional, Serrano, Jillian, additional, Singer-Berk, Moriel, additional, Weisburd, Ben, additional, Wilson, Michael, additional, Austin-Tse, Christina, additional, Abdelhakim, Marwa, additional, Althagafi, Azza, additional, Babbi, Giulia, additional, Bellazzi, Riccardo, additional, Bovo, Samuele, additional, Carta, Maria Giulia, additional, Casadio, Rita, additional, Coenen, Pieter-Jan, additional, De Paoli, Federica, additional, Floris, Matteo, additional, Gajapathy, Manavalan, additional, Hoehndorf, Robert, additional, Jacobsen, Julius O.B., additional, Joseph, Thomas, additional, Kamandula, Akash, additional, Katsonis, Panagiotis, additional, Kint, Cyrielle, additional, Lichtarge, Olivier, additional, Limongelli, Ivan, additional, Lu, Yulan, additional, Magni, Paolo, additional, Mamidi, Tarun Karthik Kumar, additional, Martelli, Pier Luigi, additional, Mulargia, Marta, additional, Nicora, Giovanna, additional, Nykamp, Keith, additional, Pejaver, Vikas, additional, Peng, Yisu, additional, Pham, Thi Hong Cam, additional, Podda, Maurizio S., additional, Rao, Aditya, additional, Rizzo, Ettore, additional, Saipradeep, Vangala G, additional, Savojardo, Castrense, additional, Schols, Peter, additional, Shen, Yang, additional, Sivadasan, Naveen, additional, Smedley, Damian, additional, Soru, Dorian, additional, Srinivasan, Rajgopal, additional, Sun, Yuanfei, additional, Sunderam, Uma, additional, Tan, Wuwei, additional, Tiwari, Naina, additional, Wang, Xiao, additional, Wang, Yaqiong, additional, Williams, Amanda, additional, Worthey, Elizabeth A., additional, Yin, Rujie, additional, You, Yuning, additional, Zeiberg, Daniel, additional, Zucca, Susanna, additional, Bakolitsa, Constantina, additional, Brenner, Steven E., additional, Fullerton, Stephanie M, additional, Radivojac, Predrag, additional, Rehm, Heidi L., additional, and O’Donnell-Luria, Anne, additional

Published
2023
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46. Perspective: Improving Nutritional Guidelines for Sustainable Health Policies: Current Status and Perspectives

Author

Magni, Paolo, Bier, Dennis M, Pecorelli, Sergio, Agostoni, Carlo, Astrup, Arne, Brighenti, Furio, Cook, Robert, Folco, Emanuela, Fontana, Luigi, Gibson, Robert A, Guerra, Ranieri, Guyatt, Gordon H, Ioannidis, John PA, Jackson, Ann S, Klurfeld, David M, Makrides, Maria, Mathioudakis, Basil, Monaco, Alessandro, Patel, Chirag J, Racagni, Giorgio, Schünemann, Holger J, Shamir, Raanan, Zmora, Niv, and Peracino, Andrea

Published
2017
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