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  • Computational Docking and Pharmacological Evaluation of Natural Alkaloids as Safer Alternatives to Synthetic Narcotics

  • 1Kakinada Institute of Technological Sciences, Andhra Pradesh,533255
    2Narasaraopeta Institute of Pharmaceutical Sciences, Palnadu, Andhra Pradesh, 533601

Abstract

The increasing prevalence of opioid misuse and associated fatalities has highlighted an urgent need for safer analgesic alternatives. Synthetic narcotics such as morphine and fentanyl remain highly effective in clinical pain management but are associated with significant risks, including tolerance, dependence, and respiratory depression (Volkow et al., 2019). Natural alkaloids, derived from medicinal plants, possess diverse pharmacological properties and may provide novel therapeutic approaches with reduced abuse liability. In this study, computational docking and in silico pharmacological evaluations were performed on selected natural alkaloids—mitragynine, berberine, conolidine, noscapine, and papaverine—against ?-opioid (MOR), ?-opioid (KOR), and ?-opioid (DOR) receptors. Docking affinities and receptor-ligand interactions were compared to synthetic narcotics, including morphine, fentanyl, and oxycodone. Additionally, ADMET (absorption, distribution, metabolism, excretion, and toxicity) predictions were conducted to evaluate safety and drug-likeness. Results demonstrated that several alkaloids, particularly mitragynine and conolidine, exhibited strong receptor affinity with favorable pharmacokinetic properties. These findings support the hypothesis that natural alkaloids may serve as promising analgesic candidates, warranting further preclinical and clinical investigation.

Keywords

ADMET, Computational docking, Natural alkaloids, synthetic opioids, opioid receptors

Introduction

1.1 The Opioid Crisis and the Need for Alternatives

Opioid analgesics, especially morphine, oxycodone, and fentanyl, are crucial for managing moderate to severe pain. However, over the past twenty years, there has been a sharp increase in opioid misuse, dependence, and overdose deaths (Kolodny et al., 2015). In the U.S., opioid overdoses caused more than 80,000 deaths in 2022 (CDC, 2023). Similar patterns are appearing worldwide, highlighting a global public health crisis. While opioids are effective pharmacologically, they also carry serious side effects, such as tolerance, physical dependence, respiratory depression, and gastrointestinal dysmotility (Pasternak and Pan, 2013). These issues call for the development of new analgesic drugs with better safety profiles.

1.2 Natural Alkaloids as Therapeutic Candidates

Alkaloids are nitrogen-containing secondary metabolites commonly found in plants. They exhibit a broad range of pharmacological effects, including analgesic, anti-inflammatory, antimalarial, and anticancer activities (Roberts and Wink, 1998). Historically, morphine, itself an alkaloid from Papaver somniferum, revolutionized pain treatment. More recently, plant-derived alkaloids such as mitragynine (from Mitragyna speciosa), conolidine (from Tabernaemontana divaricata), and noscapine (from Papaver somniferum) have attracted interest for their analgesic properties with potentially lower abuse liability (Kruegel and Grundmann, 2018; Tarselli et al., 2011).

Mitragynine, the major active compound in kratom, acts on μ-opioid receptors but displays partial agonist activity, potentially reducing the risk of respiratory depression (Varadi et al., 2016). Conolidine, a lesser-known alkaloid, exerts analgesic effects without significant opioid receptor binding, suggesting non-traditional pain pathways (Tarselli et al., 2011). Noscapine, while historically studied as an antitussive, has shown promise in anticancer and analgesic research (Ke et al., 2000).

1.3 Computational Pharmacology Approaches

The rise of computational pharmacology has transformed early-stage drug discovery. Molecular docking offers a rapid and cost-effective method for predicting ligand-receptor interactions and estimating binding affinities (Morris et al., 2009). Coupled with ADMET profiling, researchers can prioritize compounds with favorable drug-like properties before investing in resource-intensive laboratory and clinical studies (Daina et al., 2017). Such approaches have been widely applied to natural products in drug discovery pipelines (Ekins et al., 2007).

1.4 Aim of the Study

This study aims to evaluate the analgesic potential of selected natural alkaloids using computational docking and pharmacological profiling. Specifically, the objectives are:

  1. To assess binding affinities of natural alkaloids against opioid receptors (MOR, KOR, DOR).
  2. To compare interaction profiles with standard synthetic narcotics (morphine, fentanyl, oxycodone).
  3. To evaluate pharmacokinetic and toxicity parameters using in silico ADMET tools.
    Through this approach, we provide insights into the viability of natural alkaloids as safer alternatives to synthetic narcotics.

2. MATERIALS AND METHODS

2.1 Selection of Compounds

Five natural alkaloids were selected based on literature evidence of analgesic or central nervous system activity: mitragynine, berberine, conolidine, noscapine, and papaverine. These compounds represent structurally diverse plant alkaloids with reported bioactivity in pain modulation or neuropharmacology (Kruegel and Grundmann, 2018; Tarselli et al., 2011).

As controls, three widely used synthetic narcotics were included: morphine, fentanyl, and oxycodone. Their well-characterized receptor interactions and clinical analgesic potency provided a benchmark for comparison (Pasternak and Pan, 2013).

Chemical structures were retrieved from PubChem in SDF format and optimized for docking by energy minimization using the MMFF94 force field in PyRx 0.8 (Dallakyan and Olson, 2015).

2.2 Protein Target Preparation

Crystal structures of human opioid receptors were obtained from the Protein Data Bank (PDB):

  • μ-opioid receptor (MOR): PDB ID 5C1M
  • κ-opioid receptor (KOR): PDB ID 6VI4
  • δ-opioid receptor (DOR): PDB ID 4EJ4

Proteins were prepared using AutoDock Tools by removing water molecules, adding missing hydrogens, and assigning Gasteiger charges. Binding pockets were defined based on co-crystallized ligands and literature data on opioid receptor active sites (Manglik et al., 2012).

2.3 Molecular Docking Procedure

Docking simulations were carried out using AutoDock Vina, which applies a gradient optimization method to predict binding free energy (Trott and Olson, 2010).

  • Grid box size: 25 × 25 × 25 Å centered on the ligand-binding pocket.
  • Exhaustiveness: 8 (default setting, balancing accuracy and speed).
  • Scoring function: Binding affinity expressed as free energy (ΔG, kcal/mol).

Docking poses were ranked by lowest binding free energy, and interaction analysis was performed with Discovery Studio Visualizer.

2.4 ADMET and Drug-Likeness Prediction

Pharmacokinetic and toxicity parameters were assessed using SwissADME (Daina et al., 2017), pkCSM (Pires et al., 2015), and ProTox-II (Banerjee et al., 2018). Evaluated properties included:

  • Drug-likeness: Lipinski’s Rule of Five compliance.
  • Absorption: Gastrointestinal (GI) absorption and blood–brain barrier (BBB) penetration.
  • Distribution and metabolism: Cytochrome P450 inhibition profiles.
  • Toxicity: Hepatotoxicity, mutagenicity, carcinogenicity predictions.

3. RESULTS

3.1 Docking Affinities

Table 1 summarizes binding affinities (ΔG, kcal/mol) of alkaloids and synthetic narcotics across the three receptors.

Table 1. Docking scores (kcal/mol) of natural alkaloids and synthetic narcotics against opioid receptors.

Compound

μ-Opioid (MOR)

κ-Opioid (KOR)

δ-Opioid (DOR)

Mitragynine

-9.1

-8.6

-8.3

Conolidine

-8.7

-8.2

-7.9

 

-8.3

-8.1

-7.8

Noscapine

-8.0

-7.7

-7.4

Papaverine

-7.6

-7.4

-7.2

Morphine

-9.4

-8.9

-8.5

Fentanyl

-10.2

-9.6

-9.1

Oxycodone

-8.8

-8.3

-8.0

Synthetic narcotics demonstrated the strongest binding, particularly fentanyl at MOR (−10.2 kcal/mol). However, mitragynine and conolidine showed comparable affinities, suggesting potential efficacy as analgesic ligands.

3.2 Receptor–Ligand Interaction Profiles

  • Mitragynine: At MOR, formed hydrogen bonding with Asp147 and hydrophobic interactions with Trp318, key residues involved in opioid agonism (Manglik et al., 2012).
  • Conolidine: Engaged in π-π stacking with Tyr148 and van der Waals interactions with His297 at MOR.
  • Berberine: Exhibited moderate binding with mixed hydrogen bonding and hydrophobic contacts.
  • Noscapine and Papaverine: Displayed weaker binding, consistent with their lower docking scores.
  • Synthetic narcotics: As expected, morphine and fentanyl displayed strong hydrogen bonding with Asp147 and Tyr326, reinforcing their high receptor affinity (Pasternak and Pan, 2013).

3.3 ADMET Predictions

Table 2. Pharmacokinetic and toxicity profiles of selected compounds.

Compound

GI Absorption

BBB Penetration

Hepatotoxicity

Mutagenicity

Rule of Five

Mitragynine

High

Yes

No

No

Pass

Conolidine

High

Yes

No

No

Pass

Berberine

Low

No

Possible

No

Fail (MW>500)

Noscapine

Moderate

Yes

No

No

Pass

Papaverine

Moderate

Yes

No

No

Pass

Morphine

High

Yes

No

No

Pass

Fentanyl

High

Yes

Yes

Possible

Pass

Oxycodone

High

Yes

No

No

Pass

  • Mitragynine and conolidine: Favorable pharmacokinetic profiles, high GI absorption, BBB penetration, and low predicted toxicity.
  • Berberine: Poor absorption and potential hepatotoxicity limit its suitability.
  • Synthetic narcotics: High BBB penetration but with elevated toxicity risks, especially fentanyl.

3.4 Comparative Insights

  • Binding affinities of mitragynine (−9.1 kcal/mol at MOR) and conolidine (−8.7 kcal/mol) were close to morphine (−9.4 kcal/mol).
  • ADMET profiles suggest natural alkaloids may provide safer pharmacological properties compared to synthetic narcotics, especially regarding toxicity and addiction liability.

4. DISCUSSION

4.1 Comparison of Natural Alkaloids with Synthetic Narcotics

The results demonstrate that certain natural alkaloids, particularly mitragynine and conolidine, display binding affinities at the μ-opioid receptor comparable to morphine and oxycodone. While fentanyl unsurprisingly exhibited the strongest binding across all three receptors, its high potency is closely linked to increased risks of respiratory depression and overdose mortality (Volkow et al., 2019).

In contrast, mitragynine’s partial agonist activity at MOR and ability to interact with KOR and DOR suggest a broader pharmacological profile that may confer analgesia with reduced risk of respiratory depression and dependence (Varadi et al., 2016). Similarly, conolidine has been reported to produce analgesic effects through atypical mechanisms, possibly involving atypical chemokine receptor 3 (ACKR3), further reducing its risk of opioid-like side effects (Mendis et al., 2019).

These findings align with earlier reports highlighting that natural products often interact with multiple biological pathways, offering polypharmacological benefits compared to single-target synthetic drugs (Harvey et al., 2015).

4.2 ADMET Insights and Drug-Likeness

Pharmacokinetic profiling revealed that mitragynine and conolidine possess favorable oral bioavailability and blood–brain barrier penetration, critical for central analgesic action. Importantly, both compounds passed Lipinski’s Rule of Five, indicating good drug-likeness (Lipinski et al., 2001).

Conversely, berberine displayed poor gastrointestinal absorption and potential hepatotoxicity, consistent with prior reports of its limited oral bioavailability (Liu et al., 2016). Although noscapine and papaverine exhibited moderate binding and acceptable ADMET profiles, their comparatively weaker affinities suggest limited potential as primary analgesics.

Synthetic opioids showed high BBB penetration and favorable absorption, but their toxicity predictions (particularly for fentanyl) highlight the clinical trade-off between potency and safety (Kalso et al., 2003).

4.3 Clinical Relevance and Safety Considerations

The opioid crisis has fueled a demand for safer analgesics. Natural alkaloids may provide a valuable alternative by:

  1. Reducing abuse liability: Partial agonists like mitragynine may not fully activate MOR pathways, lowering euphoric effects associated with addiction (Kruegel and Grundmann, 2018).
  2. Alternative mechanisms: Conolidine’s action on non-opioid pathways suggests novel analgesic mechanisms that could bypass classical opioid-related risks (Tarselli et al., 2011).
  3. Reduced toxicity: In silico ADMET predictions indicate lower hepatotoxicity and mutagenicity for most natural alkaloids compared to fentanyl.

However, potential risks must not be overlooked. For example, mitragynine has been associated with dependence in chronic kratom users, though at substantially lower risk compared to synthetic opioids (Henningfield et al., 2018). Thus, preclinical validation and clinical trials remain essential to confirm efficacy and safety.

4.4 Limitations of the Study

This research is limited by its in silico nature. While molecular docking and ADMET predictions provide valuable early insights, they cannot fully capture the complexities of biological systems. Limitations include:

  • Docking approximations: Binding affinity predictions may not directly translate to in vivo efficacy (Kitchen et al., 2004).
  • ADMET predictions: Computational models may overlook rare but clinically significant adverse effects.
  • Lack of experimental validation: In vitro binding assays and animal studies are necessary to confirm pharmacological activity.

Nevertheless, computational approaches serve as a cost-effective screening tool to prioritize candidates for further experimental research.

CONCLUSION

This study highlights the promise of natural alkaloids as safer alternatives to synthetic narcotics. Mitragynine and conolidine demonstrated strong opioid receptor binding, favorable pharmacokinetic properties, and reduced predicted toxicity compared to conventional narcotics such as fentanyl. These results suggest that natural alkaloids represent a valuable starting point for developing next-generation analgesics with lower abuse potential. While synthetic opioids remain highly potent, their associated risks underscore the need for natural product-derived scaffolds with improved safety. Future research should integrate computational findings with laboratory assays and clinical investigations to advance these compounds toward therapeutic application.

FUTURE PERSPECTIVES

  1. Experimental Validation: Radioligand binding assays and functional studies (e.g., GTPγS binding) are needed to verify receptor activity of alkaloids.
  2. In Vivo Studies: Animal models of nociception and chronic pain will help determine efficacy and safety profiles.
  3. Formulation Research: Solubility enhancement strategies (e.g., nanoparticle delivery) could improve bioavailability of poorly absorbed alkaloids like berberine.
  4. Clinical Trials: If preclinical findings support efficacy, early-phase human trials should evaluate analgesic effects, dependence potential, and toxicity.
  5. Polypharmacology Exploration: Investigating multi-target effects may uncover unique therapeutic niches beyond opioid receptor interactions.

By combining traditional pharmacognosy with modern computational pharmacology, novel plant-derived alkaloids may contribute significantly to the next generation of safer analgesics.

REFERENCES

  1. Banerjee, P. et al. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263.
  2. CDC (2023). Drug Overdose Deaths in the United States, 2001–2022. Centers for Disease Control and Prevention.
  3. Daina, A. et al. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness, and medicinal chemistry friendliness. Scientific Reports, 7, 42717.
  4. Dallakyan, S. and Olson, A.J. (2015). Small-molecule library screening by docking with PyRx. Chemical Biology, 1263, 243–250.
  5. Ekins, S. et al. (2007). In silico pharmacology for drug discovery: Applications to targets and beyond. British Journal of Pharmacology, 152(1), 21–37.
  6. Harvey, A.L. et al. (2015). The re-emergence of natural products for drug discovery in the genomics era. Nature Reviews Drug Discovery, 14(2), 111–129.
  7. Henningfield, J.E. et al. (2018). Kratom pharmacology: Implications for abuse potential regulation. Neuropharmacology, 134(Pt A), 174–182.
  8. Kalso, E. et al. (2003). Opioids in chronic non-cancer pain: Systematic review of efficacy and safety. Pain, 112(3), 372–380.
  9. Ke, Y. et al. (2000). Noscapine targets tubulin and inhibits angiogenesis. Cancer Research, 60(14), 3891–3896.
  10. Kitchen, D.B. et al. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews Drug Discovery, 3(11), 935–949.
  11. Kolodny, A. et al. (2015). The prescription opioid and heroin crisis: A public health approach to an epidemic of addiction. Annual Review of Public Health, 36, 559–574.
  12. Kruegel, A.C. and Grundmann, O. (2018). The medicinal chemistry and neuropharmacology of kratom: A preliminary discussion of a promising medicinal plant and analysis of its potential for abuse. Neuropharmacology, 134(Pt A), 108–120.
  13. Lipinski, C.A. et al. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development. Advanced Drug Delivery Reviews, 46(1-3), 3–26.
  14. Liu, C.S. et al. (2016). Berberine in the treatment of type 2 diabetes mellitus: A systemic review and meta-analysis. Evidence-Based Complementary and Alternative Medicine, 2016, 1–12.
  15. Manglik, A. et al. (2012). Crystal structure of the μ-opioid receptor bound to a morphinan antagonist. Nature, 485(7398), 321–326.
  16. Mendis, G.D. et al. (2019). Conolidine is a novel analgesic that targets atypical chemokine receptor ACKR3. Nature Communications, 10, 1–13.
  17. Morris, G.M. et al. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791.
  18. Pasternak, G.W. and Pan, Y.X. (2013). Mu opioids and their receptors: Evolution of a concept. Pharmacological Reviews, 65(4), 1257–1317.
  19. Pires, D.E.V. et al. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072.
  20. Roberts, M.F. and Wink, M. (1998). Alkaloids: Biochemistry, Ecology, and Medicinal Applications. Springer Science & Business Media.
  21. Tarselli, M.A. et al. (2011). Conolidine: A potent non-opioid analgesic from the flowering plant Tabernaemontana divaricata. Nature Chemistry, 3(6), 537–541.
  22. Trott, O. and Olson, A.J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461.
  23. Varadi, A. et al. (2016). Mitragynine/ Corynantheidine Pseudoindoxyls as potent analgesics with mu opioid receptor agonism and delta opioid receptor antagonism. Journal of Medicinal Chemistry, 59(18), 8381–8397.
  24. Volkow, N.D. et al. (2019). The neuroscience of drug reward and addiction. Physiological Reviews, 99(4), 2115–2140.

Reference

  1. Banerjee, P. et al. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263.
  2. CDC (2023). Drug Overdose Deaths in the United States, 2001–2022. Centers for Disease Control and Prevention.
  3. Daina, A. et al. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness, and medicinal chemistry friendliness. Scientific Reports, 7, 42717.
  4. Dallakyan, S. and Olson, A.J. (2015). Small-molecule library screening by docking with PyRx. Chemical Biology, 1263, 243–250.
  5. Ekins, S. et al. (2007). In silico pharmacology for drug discovery: Applications to targets and beyond. British Journal of Pharmacology, 152(1), 21–37.
  6. Harvey, A.L. et al. (2015). The re-emergence of natural products for drug discovery in the genomics era. Nature Reviews Drug Discovery, 14(2), 111–129.
  7. Henningfield, J.E. et al. (2018). Kratom pharmacology: Implications for abuse potential regulation. Neuropharmacology, 134(Pt A), 174–182.
  8. Kalso, E. et al. (2003). Opioids in chronic non-cancer pain: Systematic review of efficacy and safety. Pain, 112(3), 372–380.
  9. Ke, Y. et al. (2000). Noscapine targets tubulin and inhibits angiogenesis. Cancer Research, 60(14), 3891–3896.
  10. Kitchen, D.B. et al. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews Drug Discovery, 3(11), 935–949.
  11. Kolodny, A. et al. (2015). The prescription opioid and heroin crisis: A public health approach to an epidemic of addiction. Annual Review of Public Health, 36, 559–574.
  12. Kruegel, A.C. and Grundmann, O. (2018). The medicinal chemistry and neuropharmacology of kratom: A preliminary discussion of a promising medicinal plant and analysis of its potential for abuse. Neuropharmacology, 134(Pt A), 108–120.
  13. Lipinski, C.A. et al. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development. Advanced Drug Delivery Reviews, 46(1-3), 3–26.
  14. Liu, C.S. et al. (2016). Berberine in the treatment of type 2 diabetes mellitus: A systemic review and meta-analysis. Evidence-Based Complementary and Alternative Medicine, 2016, 1–12.
  15. Manglik, A. et al. (2012). Crystal structure of the μ-opioid receptor bound to a morphinan antagonist. Nature, 485(7398), 321–326.
  16. Mendis, G.D. et al. (2019). Conolidine is a novel analgesic that targets atypical chemokine receptor ACKR3. Nature Communications, 10, 1–13.
  17. Morris, G.M. et al. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791.
  18. Pasternak, G.W. and Pan, Y.X. (2013). Mu opioids and their receptors: Evolution of a concept. Pharmacological Reviews, 65(4), 1257–1317.
  19. Pires, D.E.V. et al. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072.
  20. Roberts, M.F. and Wink, M. (1998). Alkaloids: Biochemistry, Ecology, and Medicinal Applications. Springer Science & Business Media.
  21. Tarselli, M.A. et al. (2011). Conolidine: A potent non-opioid analgesic from the flowering plant Tabernaemontana divaricata. Nature Chemistry, 3(6), 537–541.
  22. Trott, O. and Olson, A.J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461.
  23. Varadi, A. et al. (2016). Mitragynine/ Corynantheidine Pseudoindoxyls as potent analgesics with mu opioid receptor agonism and delta opioid receptor antagonism. Journal of Medicinal Chemistry, 59(18), 8381–8397.
  24. Volkow, N.D. et al. (2019). The neuroscience of drug reward and addiction. Physiological Reviews, 99(4), 2115–2140.

Photo
Kumbha Ravindra
Corresponding author

Department of Pharmaceutical Engineering, Kakinada Institute of Technological Sciences, Andhra Pradesh,533255

Photo
Ganta Lakshmana
Co-author

Department of Pharmacology, Narasaraopeta Institute of Pharmaceutical Sciences, Palnadu, Andhra Pradesh, 533601

Kumbha Ravindra, Ganta Lakshmana, Computational Docking and Pharmacological Evaluation of Natural Alkaloids as Safer Alternatives to Synthetic Narcotics, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 9, 3326-3332. https://doi.org/10.5281/zenodo.17223260

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