Discovering Drugs for Chronic Pain: Rising to the Challenge

Drug discovery is a lengthy and expensive process; however, computational modeling and AI now offer unprecedented opportunities for faster discovery. A field still in urgent need of new treatments, however, is chronic pain.

Chronic pain, defined as pain persisting for more than three months, is estimated to affect almost one-third of the global population. It leads to reduced quality of life, disability and psychological comorbidities such as depression and anxiety, while having major economic, healthcare system and societal impacts.¹

Current treatments aim to block the pain signal at all levels of transmission from the periphery to the primary somatosensory cortex area of the brain, where pain is perceived.¹ Many chronic pain medications have an unspecific mechanism of action, depressing the central nervous system to cause side effects such as reduced mobility and impaired memory.²

While opioids are effective for chronic pain relief, there is a human cost to their use. Opioid associated mortality has quadrupled since the turn of the century, with adverse side effects including tolerance, addiction and increased pain sensitivity.¹

With limited efficacy and a common theme of adverse side effects, there remains a significant unmet clinical need for effective treatments for chronic pain.

Dr. Christopher L. Robinson, a regenerative and pain physician-scientist at The Johns Hopkins University School of Medicine, explained why drug discovery and development for chronic pain is so challenging: “Pain is not just a single target or defect. There are numerous pain signaling pathways, receptors and underlying causes of pain. Some of the aforementioned are involved in other critical processes, so inhibiting one of them can lead to off-target effects, but does not mean we are not trying.”

Also addressing the challenge of chronic pain treatment, David Bennett, a professor of neurology and neurobiology, and Steven Middleton, a postdoctoral researcher, both at the University of Oxford, added: “Chronic pain is a very heterogeneous condition likely reflecting multiple mechanisms at play, and not all drugs will work for all types of chronic pain. That's why detailed patient phenotyping when recruiting for clinical trials is really important.”

“Pain-relief can engage the reward system, so it is then an added challenge to develop new and effective analgesics that have no side effects, such as addiction,” they continued.

Despite these significant challenges, scientists worldwide are exploring new mechanisms and targets for pain relief.

Non-opioid alternatives

Many emerging treatments focus on modulating opioid receptors to achieve the same pain relief as opioids without the adverse side effects. One such area of research explores the activation of different opioid receptors, specifically the delta-opioid receptor (DOR), as opposed to the main opioid receptor, mu. DOR is widely distributed across pain-processing areas, but is also present in the amygdala, which is responsible for emotional processing, so it may modulate the emotional association of pain.¹

Molecules such as LIM Kinase, RSG4 and GPCRs all aim to increase expression of DOR and are either in preclinical trials or under investigation.¹ The mechanism of action of RSG4 is to prolong the natural analgesic effects of endogenous opioids, removing the need for exogenous sources and their unwanted side effects. Another molecule that enhances naturally occurring endogenous opioids is granulocyte-colony stimulating factor (G-CSF), which increases local neutrophils, leading to a greater secretion of endogenous opioids for pain relief.

Sodium channel inhibitors

Robinson, Bennett and Middleton, all experts in this field, believe that selective sodium channel inhibitors, such as the recently FDA-approved drug Suzetrigine, are the most significant development in pain drug discovery in recent times. In the central nervous system (CNS), sodium channels are essential for neuronal excitability and signal transmission. Certain subtypes of sodium channels (e.g., Nav1.7, Nav1.8, Nav1.9) have specific roles in pain signal transmission, offering potential drug target options.³ Suzetrigine, a Nav1.8 channel blocker, is the first non-opioid drug for moderate to severe acute pain to be approved in decades, and studies are ongoing to assess its use in chronic pain.³

Gene therapy

An alternative approach in emerging pain treatments is gene therapy. In preclinical trials, DNA vectors have shown success inhibiting leukocyte elastase to reduce neuropathic pain in mice.¹ Two treatments in development have also exhibited beneficial effects for other diseases, offering the possibility of dual therapy. Although these early-stage therapies require further preclinical trials and human studies to assess safety and efficacy, they offer new avenues for chronic pain treatment development.

Pain-related genes

The recent discovery from Bennett’s laboratory of a pain-related gene (SLC45A4) unveils a new therapeutic target and pathway in chronic pain. Their preclinical studies indicate a role for polyamines in chronic pain, while clinical studies show increased levels of polyamines in rheumatoid arthritis and inflammation.⁴

Optimistic about the future, Bennett and Middleton remarked, “If our future research continues to support SLC45A4 and polyamine regulation as a key drug target, then it is possible patients suffering from chronic pain may one day see these new drugs in clinics, but we have many hurdles to tackle first.”

Cannabinoid receptors

For the first time, scientists have designed a compound that binds to cannabinoid receptor type 1 (CB1), eliciting analgesia without the unwanted psychoactive effects.⁵ Using 3D computational modeling to identify a novel cryptic pocket (a binding area in the receptor that appears transiently), the researchers from Stanford University and Washington University then rationally designed a compound to bind selectively and not trigger unwanted secondary effects in the CNS. “The drug we designed here holds a great promise; it reduces dependency on opioids for chronic pain and offers a non-addictive alternative,” remarked Vipin Rangari, a scientist at Washington University School of Medicine and lead author of the paper.

Targeting novel pain pathways

Researchers at the University of Aberdeen recently discovered a new pain pathway for targeting chronic pain. This pathway centers around the concept of sng pain (the Taiwanese term for soreness related to muscle acid buildup) and is related to excessive amounts of glutamate release in muscles, leading to permanent activation of pain nerves.⁶ Their findings put a spotlight on ASIC3 as a new drug target for tissue acidosis-associated chronic pain, which is common in conditions such as rheumatoid arthritis, delayed onset muscle soreness and fibromyalgia.

Psychedelics

Following successful trials of psychedelics for the treatment of depression and anxiety, they are now under investigation for use in chronic pain.⁷ This is an emerging area of interest, and more research is needed to determine if this class of drugs could be beneficial in chronic pain.

Many chronic pain treatments target symptoms. How can the root cause of such a complex problem be treated? Robinson believes the answer may lie in regenerative medicine and stem cell-based treatments: “We are now able to grow tissues and organs in the lab, and in the near future, we will one day regrow your own cartilage from your own cells by utilizing induced pluripotent stem cells (IPSCs), not to be confused with embryonic stem cells. So, it is you curing you via the beauty of IPSCs-based science.”  

Harnessing innovative technologies in chronic pain management and drug discovery

With new treatment options for chronic pain scarce and still under development, scientists have turned to innovative technologies such as virtual reality (VR), wearable medical technologies and AI.

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Virtual reality distracts the patient from the perception of pain and facilitates neural reprocessing or retraining the brain to interpret pain differently. VR studies in chronic lower back pain demonstrate pain reduction, whereas wearable medical technology research reveals reduced depression and opioid use.⁸ Such wearable technology also gives clinicians insight into daily physiological data readouts in chronic pain, aiding their understanding of the condition.

The unique ability of AI to interrogate large volumes of data is accelerating drug discovery in terms of target identification, virtual screening, biomarker discovery, drug repurposing identification and prediction of pharmacokinetic properties and toxicity.⁹ “[…] small molecule drug discovery is a feat in itself, but AI-driven drug modelling is expediting the processes by narrowing down the hits,” explained Robinson.

However, AI faces challenges of data scarcity, limited training options, a lack of standardization and intense resource requirements.⁹ Despite these, AI’s rapid advancement offers hope of faster drug discovery and a net benefit for health research in the future.

Using computational modeling to identify cryptic pockets offers a novel method for discovering therapeutic targets that would have otherwise remained hidden. Computer-aided drug discovery applications can significantly reduce the number of candidate molecules to evaluate, therefore expediting the process.¹⁰

“[…] exciting advancements in drug discovery include rapid advancements in structural biology, specifically cryoEM. Target structures from cryoEM can be used to rationally tune and discover molecules that are effective in their therapeutic purpose while avoiding off-target or side effects,” explained Evan O’Brien, an assistant professor at The Johns Hopkins University School of Medicine. “Further, next-gen screening approaches are rapidly replacing ‘conventional’ library screenings. Ultra-large ‘barcoded’ libraries of small molecules allow for fast discovery of new lead compounds, and computational docking approaches (again leveraging the above experimental advances in cryoEM) allow for computers to do the high-throughput discovery for us.”

Machine learning can also be coupled with proteomic profiling, enabling the identification of protein biomarkers in chronic pain.¹¹ After decades of little progress in chronic pain, research aided by technological innovations enabling a new era of drug discovery, revealing new mechanisms, therapeutic targets and compounds. It is hoped these breakthroughs will soon lead to treatments for millions of chronic pain sufferers worldwide.