Dr Giovanni Bottegoni

Dr Giovanni Bottegoni

School of Pharmacy
Senior Lecturer in Computational Medicinal Chemistry

Contact details

Address
Institute of Clinical Sciences
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Giovanni Bottegoni is a computational medicinal chemist, combining over ten years’ experience in computational drug discovery with entrepreneurial and managerial experiences.

His goal is to thrive within an “open innovation” environment, where academia and industry meet, interact, and mutually profit.

His scientific interests are in structure-based drug design, computer-assisted drug design and polypharmacology.

Qualifications

  • Lecturer in Computational Medicinal Chemistry
  • MSc (cum laude) in International Healthcare Management, Economics, and Policy (MIHMEP), SDA Bocconi (Milan, Italy), 2015
  • PhD in Pharmaceutical Sciences, Alma Mater Studiorum University of Bologna (Bologna, Italy), 2006
  • MSc Pharmaceutical Biotechnology (cum laude), Alma Mater Studiorum University of Bologna (Bologna, Italy), 2002

Biography

Giovanni Bottegoni began his scientific career with a master of science in pharmaceutical biotechnology. In 2006, he obtained his doctorate in medicinal chemistry from the Alma Mater Studiorum – University of Bologna, Faculty of Pharmacy.

For two years, he was a post-doctoral research fellow at the Scripps Research Institute in La Jolla, CA – USA in the laboratory of Prof. Ruben Abagyan. SCARE and 4D-docking, the computational methods that he contributed to develop while at Scripps, are now a standard part of the ICMPro molecular modelling package (Molsoft L.L.C. San Diego, CA – USA) and have been independently and successfully applied in drug discovery.

Later, he joined the Istituto Italiano di Tecnologia (IIT), first as a postdoctoral fellow and eventually as a team leader at the Dept. of Drug Discovery and Development – Computation, gaining experience in coordinating preclinical drug discovery projects.

Since 2014, with the goal of thriving within the “open innovation” environment described by Henry Chesbrough, Dr. Bottegoni has been augmenting his scientific expertise with managerial skills. He obtained a certificate of advanced studies (CAS) in Management of Biotech, MedTech, and Pharma Ventures at EPFL, Lausanne (CH) and then undertook a full-time Master in International Healthcare Management at SDA Bocconi in Milan (MIHMEP15), graduating cum laude.

In May 2014, he co-founded BiKi Technologies (IT), serving as CEO and business development manager at this innovative start-up, which develops and commercializes software based on molecular dynamics.

He spent two years (2017 – 2018) as Senior Scientist in the computational chemistry group at Heptares Therapeutics (now SoseiHeptares), developing MD-based drug discovery protocols to deliver actionable knowledge for synthetic chemistry in hit-to-lead and lead-optimization campaigns.

Since December 2018, ha has joined the School of Pharmacy at the University of Birmingham (UK) as senior lecturer in computational medicinal chemistry. He is the P.I. in the Computer-assisted Molecular Design lab.

Dr. Bottegoni co-authored over 50 scientific studies published in international peer-reviewed journals and books and is co-inventor of four patents. In 2015, he received the DCF Prize for Medicinal Chemistry, which the Medicinal Chemistry Division of the Italian Chemical Society awards to a young scientist working in industry. He has very recently been acknowledged as most meritorious runner-up for the EFMC prize for a young medicinal chemist in industry 2018.

Teaching

  • MPharm
    • Chemistry for Pharmacists: years 1 & 2

Postgraduate supervision

Drug Discovery, Computer-assisted Drug Design, Molecular Dynamics, GPCR Rational Drug Design, Structure-based Drug Design, Ligand Docking, Virtual Ligand Screening, Allosteric Pocket Detection, In Silico Drug Discovery, Computational Molecular Modelling

Research

Small molecule development for limited ligandability targets. A limited ligandability target is a target that is druggable (i.e. its modulation can elicit a pharmacological effect), but particularly hard to modulate with small organic molecules (Surade & Blundell, 2012). The key idea is to exploit the possibilities offered by target flexibility generating conformational populations of selected targets by ‘brute-force’, GPU-accelerated molecular dynamics (MD), or by enhanced sampling techniques that do not make any assumption about the nature of the slow (relevant) degrees of freedom in the system. These conformers will be used in combination with flexible receptor-docking and virtual screening strategies to identify novel putative ligands that specifically target unprecedented rearrangements of the protein target.

Molecular Dynamics and Related Methods for Membrane Proteins. Membrane proteins (transporters, GPCRs, ion channels, etc.) are key drug targets. However, a rigorous treatment of membrane dynamics and composition requires great computational resources. Therefore, it is often neglected in drug discovery programs. Here, previouslyb developed strategies (Ferraro et al., 2015; Ferraro et al., 2017; Robertson et al., 2018) would be used (and, ideally, further refined) to target membrane proteins in order to improve the outcome of the drug discovery programs.

In Silico Polypharmacology. A selective polypharmacological profile of a new chemical entity – i.e., its ability to concurrently modulate multiple but rationally selected targets – may provide drug candidates with a superior efficacy profile compared to traditional single-target molecules. The idea of a multi-target approach is particularly appealing for the treatment of complex and multifactorial diseases. Recently, I have focused on drug addiction, in particular tobacco smoking. In the polypharmacology framework, my idea has been to conceive single molecular entities that are able to simultaneously modulate two targets implicated in addiction: Dopamine D3 receptor (D3DR) and FAAH (De Simone et al., Chem Comm 2014; Micoli et al., MedChemComm 2016; De Simone et al., J Med Chem 2017). I am now planning to apply the same drug discovery protocol encompassing modelling, synthesis and in vitro/vivo testing to new and unprecedented target combinations. In particular, I am interested in exploring possible application in immunoncology and NAFLD.

 

 

Other activities

In 2014, Dr Bottegoni co-founded BiKi Technologies, a start-up company that commercializes software solutions for computational medicinal chemistry and, for two years, was CEO of the company (http://www.bikitech.com).

Publications

Recent publications

Article

Redenti, S, Marcovih, I, De Vita, T, Perez, C, De Zorzi, R, Demitri, N, Perez, DI, Bottegoni, G, Bisignano, P, Bissaro, M, Moro, S, Martinez, A, Storici, P, Spalluto, G, Cavalli, A & Federico, S 2019, 'A triazolotriazine-based dual GSK-3β/CK-1δ ligand as a potential neuroprotective agent presenting two different mechanisms of enzymatic inhibition', ChemMedChem, vol. 14, no. 3, pp. 310-314. https://doi.org/10.1002/cmdc.201800778

Decherchi, S, Bottegoni, G, Spitaleri, A, Rocchia, W & Cavalli, A 2018, 'BiKi Life Sciences: A New Suite for Molecular Dynamics and Related Methods in Drug Discovery', Journal of Chemical Information and Modeling, vol. 58, no. 2, pp. 219-224. https://doi.org/10.1021/acs.jcim.7b00680

Robertson, N, Rappas, M, Doré, AS, Brown, J, Bottegoni, G, Koglin, M, Cansfield, J, Jazayeri, A, Cooke, RM & Marshall, FH 2018, 'Structure of the complement C5a receptor bound to the extra-helical antagonist NDT9513727', Nature, vol. 553, pp. 111-114. https://doi.org/10.1038/nature25025

Simoni, E, Bartolini, M, Abu, IF, Blockley, A, Gotti, C, Bottegoni, G, Caporaso, R, Bergamini, C, Andrisano, V, Cavalli, A, Mellor, IR, Minarini, A & Rosini, M 2017, 'Multitarget drug design strategy in Alzheimer's disease: Focus on cholinergic transmission and amyloid-β aggregation', Future Medicinal Chemistry, vol. 9, no. 10, pp. 953-963. https://doi.org/10.4155/fmc-2017-0039

De Simone, A, Russo, D, Ruda, GF, Micoli, A, Ferraro, M, Di Martino, RMC, Ottonello, G, Summa, M, Armirotti, A, Bandiera, T, Cavalli, A & Bottegoni, G 2017, 'Design, Synthesis, Structure-Activity Relationship Studies, and Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) Modeling of a Series of O-Biphenyl Carbamates as Dual Modulators of Dopamine D3 Receptor and Fatty Acid Amide Hydrolase', Journal of Medicinal Chemistry, vol. 60, no. 6, pp. 2287-2304. https://doi.org/10.1021/acs.jmedchem.6b01578

Ferraro, M, Masetti, M, Recanatini, M, Cavalli, A & Bottegoni, G 2016, 'Mapping cholesterol interaction sites on serotonin transporter through coarse-grained molecular dynamics', PLoS ONE, vol. 11, no. 12, e0166196. https://doi.org/10.1371/journal.pone.0166196

Diamanti, E, Bottegoni, G, Goldoni, L, Realini, N, Pagliuca, C, Bertozzi, F, Piomelli, D & Pizzirani, D 2016, 'Pyrazole-Based Acid Ceramidase Inhibitors: Design, Synthesis, and Structure-Activity Relationships', Synthesis (Germany), vol. 48, no. 17, pp. 2739-2756. https://doi.org/10.1055/s-0035-1561456

Mollica, L, Theret, I, Antoine, M, Perron-Sierra, F, Charton, Y, Fourquez, JM, Wierzbicki, M, Boutin, JA, Ferry, G, Decherchi, S, Bottegoni, G, Ducrot, P & Cavalli, A 2016, 'Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times', Journal of Medicinal Chemistry, vol. 59, no. 15, pp. 7167-7176. https://doi.org/10.1021/acs.jmedchem.6b00632

Bottegoni, G, Veronesi, M, Bisignano, P, Kacker, P, Favia, AD & Cavalli, A 2016, 'Development and Application of a Virtual Screening Protocol for the Identification of Multitarget Fragments', ChemMedChem, vol. 11, no. 12, pp. 1259-1263. https://doi.org/10.1002/cmdc.201500521

Gaspari, R, Rechlin, C, Heine, A, Bottegoni, G, Rocchia, W, Schwarz, D, Bomke, J, Gerber, HD, Klebe, G & Cavalli, A 2016, 'Kinetic and Structural Insights into the Mechanism of Binding of Sulfonamides to Human Carbonic Anhydrase by Computational and Experimental Studies', Journal of Medicinal Chemistry, vol. 59, no. 9, pp. 4245-4256. https://doi.org/10.1021/acs.jmedchem.5b01643

Chapter

Bottegoni, G & Cavalli, A 2017, Computational Methods in Multitarget Drug Discovery. in Design of Hybrid Molecules for Drug Development. Elsevier, pp. 239-258. https://doi.org/10.1016/B978-0-08-101011-2.00009-X

Other contribution

Ferraro, M, Decherchi, S, Simone, AD, Recanatini, M, Cavalli, A & Bottegoni, G 2019, Multi-Target Dopamine D3 Receptor Modulators: Actionable Knowledge for Drug Design from Molecular Dynamics and Machine Learning..

Review article

Prati, F, Bottegoni, G, Bolognesi, ML & Cavalli, A 2018, 'BACE-1 Inhibitors: From Recent Single-Target Molecules to Multitarget Compounds for Alzheimer's Disease', Journal of Medicinal Chemistry, vol. 61, no. 3, pp. 619-637. https://doi.org/10.1021/acs.jmedchem.7b00393

De Vivo, M, Masetti, M, Bottegoni, G & Cavalli, A 2016, 'Role of Molecular Dynamics and Related Methods in Drug Discovery', Journal of Medicinal Chemistry, vol. 59, no. 9, pp. 4035-4061. https://doi.org/10.1021/acs.jmedchem.5b01684

Garuti, L, Roberti, M, Bottegoni, G & Ferraro, M 2016, 'Diaryl urea: A privileged structure in anticancer agents', Current medicinal chemistry, vol. 23, no. 15, pp. 1528-1548. https://doi.org/10.2174/0929867323666160411142532

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