Systems Pharmacology
substrate-target interactions. In addition, estab- lishing the same degree of affinity for each target and minimising off-target, adverse effects are crit- ical factors to be addressed. Ramsey has argued that “
...the rational design of multi-target com- pounds is far from being an easy task, dealing with the crucial issues of selecting the right target combination, achieving a balanced activity towards them and excluding activity at the unde- sired target(s), while at the same time retaining drug-like properties”35. In order to achieve such a difficult task a variety of approaches have been employed and include22,33-35:
i) Computational ligand discovery: A variety of tools are used including chemoinformatics, virtual screening, pharmacophore development, molecular docking and dynamics studies.
ii) Quantitative Structure Activity Relationships (QSAR): Used in lead optimisation to correlate molecular structure with pharmacological and bio- logical activity.
iii) Biological validation of leads: Use of high throughput compound assessment and identifica- tion of pharmacophores.
iv) Receptor biology and binding affinities: Screening compounds against specific targets.
v) Cell-based assays: Used as a preliminary screen- ing tool.
A number of the tools and technologies used in
conventional DDD are also used in systems phar- macology drug discovery. However, the advent of the systems biology toolbox facilitated the con- struction of pathways/networks to afford the one drug-multi-target-pathway/network paradigm nec- essary for the creation of systems pharmacology drugs.
Systems pharmacology approaches Two different brief descriptions of specific target selection and drug candidate design/selection examples will serve to highlight the approaches used in the implementation of systems pharmacol- ogy. In the former case the identification of individ- ual targets and synergistic combinations of targets is critical to the systems pharmacology discovery process. Most target proteins consist of more than one structural domain, but there is a “limited repertoire of domain types”. Since protein domains mediate drug-protein interactions, Moya-Garcia
Drug Discovery World Winter 2018/19
Systems pharmacology drugs The concept of systems pharmacology drugs was first suggested in 2000. In the intervening years the academic and pharmaceutical communities have enthusiastically developed this paradigm and now a significant number of such drugs are available on the market today. Lin and colleagues noted that from 2000-15 the FDA approved a total of 361 New Molecular Entity (NME) drugs. They esti- mated that ~43% of those approved drugs had two or more targets, although to be clear such entities were not necessarily designed as systems pharma- cology drugs37. Subsequently, Ramsey and col- leagues did a similar study (2015-17) and noted that of the 101 NME drugs approved by the FDA, 34% were single target drugs, 21% were multi-tar- get drugs and 10% were combination therapies. They concluded that these numbers “...unequivo- cally supports the attractiveness...” of systems pharmacology drug strategies35.
and colleagues used this principle to guide the design of systems pharmacology drugs22. In their study they associated multi-target drugs with CATH functional families. (CATH is a database of 95 million protein domains classified into 6119 super families). They concluded that a small frac- tion of a specific functional family of proteins (CATH-FunFams) were druggable and showed using structural analyses that the domains in these families have the potential to be the druggable enti- ties within drug targets22. In the second example Thiel and co-workers out-
lined a detailed quantitative systems pharmacology (QSP) approach to determine systems pharmacolo- gy drug properties through “a mechanistic consid- eration of processes underlying drug absorption, distribution, metabolism and excretion (ADME) as well as the resulting drug action itself”, as well as “a detailed description of drug pharmacokinetics (PK) and, simultaneously, drug pharmacodynamics (PD)”36. In this study, a QSP approach was applied to quantify the drug efficacy of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors by coupling physiologically-based PK models, at the whole-body level, with affected biological networks at the cellular scale. They presented results that revealed insights about drug-induced modulation of cellular networks in a whole-body context, thereby describing complex pharmacokinetic/pharmacody- namic behaviour of COX-2 and 5-LOX inhibitors in therapeutic situations. The results demonstrated the clinical benefit of using QSP to predict drug effi- cacy and, hence, its value in helping to design opti- mal systems pharmacology drugs36.
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19Waring, SC and Naylor, S. The Silent Epidemic of Alzheimer’s Disease: Can Precision Medicine Provide Effective Drug Therapies? J. Precision Med. 4. 38-49 (2016). 20 Haupt VJ, Daminelli S and Schroeder, M. Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key. PLoS ONE 8, (2013).
http://www.plosone.org/article/ info%3Adoi%2F10.1371%2Fjou rnal.pone.0065894. 21 Hu, Y and Bajorath, J. Monitoring Drug Promiscuity Over Time. F1000Research 3, (2014), Last updated: 25 November 2018. https://f1000
research.com/articles/3-218/v2. 22 Moya-Garcia, A et al. Structural and Functional View of Polypharmacology. Nature- Scientific Reports. 7, Article Number 10102 (2017).
https://www.nature.com/article s/s41598-017-10012-x. 23 van der Greef, J and McBurney, RN. Innovation: Rescuing Drug discovery: In Vivo Pathology and Systems Pharmacology. Nature Rev. Drug Discov. 4, 961-967 (2005). 24 Clish, C et al. Integrative Biological Analysis of the APOE*-Leiden Transgenic Mouse. OMICS. 8, 3-13 (2004). 25 See as an example: Yan, Q (Editor), Systems Biology in Drug Discovery and Development: Methods and Protocols. In Methods in Molecular Biology (Volume 662). Humana Press (2016). 26 Zou, J, Zheng, M-W, Gen, Li and Su, Z-G. Advanced Systems Biology Methods in Drug Discovery and Translational Biomedicine. Biomed. Res. Intl. Article ID 742835, (2013).
http://dx.doi. org/10.1155/2013/742835. 27 Yensen, J and Naylor, S. The Complementary Iceberg Tips of Diabetes and Precision Medicine. J. Precision Med. 3, 39-57 (2016).
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