Background & Aims

It is estimated that a novel analgesic only has a 1% chance of success in clinical development (7). One of the major reasons for very high attrition rate is a lack of target validation for new analgesic mechanisms and difficulty in linking targets to the optimal patient groups (4).

Efficient target validation is difficult as systematic use of human tissue has not been implemented, current approaches to translational research are expensive and labor intensive (4).
Recent advances in high-throughput omic technologies has generated a huge wealth of data that is made available in over 1600 molecular biology databases (5). These data enable in-silico network biology which is an established paradigm to understand complex molecular interactions (8).

We have created a novel in-silico research method for linking protein research targets to >1000 pain conditions from our Pain Landscape®. Here we present the disease phenotype output from these methods for CGRP(6) and SV2a(9) and GPR18(3).

Methods

Our methods have been previously published in our patent ‘A Platform for identifying novel analgesic therapies’ WO2023058000A1 (1).

The Pain Cloud® uses a unique algorithm of high quality molecular biology databases which focus on human relevant data. The molecular protein target for any analgesic can be used as the primary input for the algorithm. A range of protein-protein molecular maps are established for the target of interest. The data output from each of the maps are analysed to generate gene ontology pathways likely modulated by the protein interactions. These molecular data are further analysed to identify the disease phenotypes based on the associated biology from the analgesic target.

We have created a novel approach by integrating outputs from these various databases to produce new data on research target to disease links. Our proprietary Pain Landscape® is a collation of 100’s of diseases related to pain including rare / orphan conditions.

Results

Example disease phenotype output are show following the input of various analgesic targets i.e. established mechanisms like CGRP, preclinical false positives like SV2a and novel analgesic targets like orphan GPCR GPR18.

Example phenotype output for CGRP (Description, Log10(P))
Migraine Disorders-16
Adrenal Gland Pheochromocytoma-15
Acute pancreatitis-15
Essential Hypertension-15
Neuroendocrine Tumors-14
Pheochromocytoma-14
Neuralgia-12
Gestational Diabetes-11
Linear atrophy-11
Pseudohypoparathyroidism, Type Ia-11
Hyperalgesia, Primary-11
Allodynia-11

Example phenotype output for SV2A (Description,Log10(P))
Epilepsy, Cryptogenic-5.4
Memory impairment-5.4
Stereotyped Behavior-4.9
Neurodevelopmental Disorders-4.5
Thin upper lip vermilion-4.3
Myocardial Ischemia-4
Mental disorders-3.9
Pheochromocytoma-3.7
Tumor necrosis-3.6
Adrenal Gland Pheochromocytoma-3.4
Forgetful-3.4
Mild cognitive disorder-3.4

Example phenotype output for GPR18 (Description Log10(P))
Chronic low back pain-7.7
Neuralgia-7.6
Acute onset pain-7.3
Agnosia for Pain-6.6
Tactile Allodynia-6
Motion Sickness-5.9
Cannabis Dependence-5.6
Skin Neoplasms-5.4
Pediatric Obesity-5.1
Cancer Pain-5
Substance Use Disorders-4.8
Cannabis use-4.7

Conclusions

To mitigate the challenges posed by traditional pain research and development we have established the Pain Cloud® platform using the Personalized Analgesics® in-silico network biology research to help link targets to disease.
We show that the platform can correctly identify the pain phenotypes associated with the licensed CGRP analgesics, including migraine. The platform also identifies no analgesic phenotypes for the SV2a protein. This was a target that demonstrated preclinical efficacy in pain models but failed in clinical development (2). Additionally, the platform identified multiple analgesic phenotypes for the orphan GPCR protein GPR18 for which there are limited published data (3).
The platform can generate patient selection criteria and/or identify a subset of patients that would be suitable for a clinical trial related to the selected therapeutic compound and the one or more identified pain phenotypes/conditions. Pain Cloud® is the first precision medicine approach to pain.

References

1.Field, MJ WO2023058000A1 – Platform for identifying novel analgesic therapies …” published patent (2023)

2.Holbech JV, Otto M, Bach FW, Jensen TS, Sindrup SH. The anticonvulsant levetiracetam for the treatment of pain in polyneuropathy: a randomized, placebo-controlled, cross-over trial. Eur J Pain. 2011 Jul;15(6):608-14.

3.Nourbakhsh F, Atabaki R, Roohbakhsh A. The role of orphan G protein-coupled receptors in the modulation of pain: A review. Life Sci. 2018 Nov 1;212:59-69.

4.Renthal et al., Human cells and networks of pain: Transforming pain target identification and therapeutic development. Neuron. 2021 May 5;109(9):1426-1429.

5.Rigden DJ, Fernández XM. The 2024 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res. 2024 Jan 5

6.Russo AF, Hay DL. CGRP physiology, pharmacology, and therapeutic targets: migraine and beyond. Physiol Rev. 2023 Apr 1;103(2):1565-1644

7.Thomas, D., Wessel, C. BIO Industry Analysis. The State of Innovation in Pain and Addiction Therapeutics, (2023)

8.Tolani P, Gupta S, Yadav K, Aggarwal S, Yadav AK. Big data, integrative omics and network biology. Adv Protein Chem Struct Biol. 2021;127:127-160.

9.Wu PP, Cao BR, Tian FY, Gao ZB. Development of SV2A Ligands for Epilepsy Treatment: A Review of Levetiracetam, Brivaracetam, and Padsevonil. Neurosci Bull. 2023 Oct 28

Presenting Author

Mark Field

Poster Authors

Mark Field

PhD, MSc

eptivA Therapeutics

Lead Author

E-mail

Topics

  • Novel Experimental/Analytic Approaches/Tools