Harm Reduction and Modified Risk Tobacco Products

Smoking is addictive and causes a number of serious diseases such as cardiovascular disease (CVD), lung cancer and chronic obstructive pulmonary disease (COPD). It is estimated that more than one billion people worldwide will continue to smoke in the foreseeable future 1.

Providing reduced-risk alternatives to adult smokers who would otherwise continue smoking cigarettes is one avenue of “tobacco harm reduction”. There has been a growing interest in recent years in harm reduction approaches to address the health risks of smoking 2,3 that would be acceptable to current smokers.

Philip Morris International is developing novel products, called potential Modified Risk Tobacco Products (MRTPs) and also referred to as Reduced Risk Products (RRPs), that may have the potential to reduce smoking-related diseases compared with cigarettes, and which deliver adult smoker satisfaction 4. To learn more about this topic, please consult the PMI science website.

The quantitative assessment of the risk reduction potential of MRTPs requires (i) the development of state of the art capabilities in regulatory and systems toxicology, (ii) a deep knowledge of the mechanisms that lead to smoking-related diseases, and (iii) expertise in the design and conduct of clinical studies aimed at substantiating reduced exposure and risk in adult smokers.

To determine whether a candidate MRTP has the potential to reduce disease risk, we compare its biological impact with that of a reference cigarette (known as 3R4F cigarette) on a mechanism-by-mechanism basis.

Fig 1: Heat-not-burn concept to reduce tobacco smoking-related disease risk.
The functioning principle of the Tobacco Heating System, or THS, allows a careful control of the temperature and of the energy transferred to the tobacco plug. This design of the THS limits the formation of HPHCs and does not initiate combustion. The temperatures reported for the THS during use are far below those required for the combustion of tobacco to occur. The key characteristics of combustion (e.g., the formation of relevant amounts of nitrogen oxides and the generation of heat) are absent in the THS.

Transparency in Science

Several studies have shown that much peer-reviewed scientific literature is not reproducible for a variety of reasons 6-11.

Contributing factors include inadequate documentation of methods and datasets and insufficient sharing of data and methods with the community, which are essential for an experiment’s replication or analysis.

This “crisis in science” calls for a significant shift to better practices 12,13.

It is crucial that the science is right, i.e. to ensure that:

  • the study is blinded
  • experiments are repeated
  • reagents are validated
  • analyses and statistical tests are appropriate
  • all results, including negative and positive controls are shown

A consistent, science-based framework should be used for identification of innovative alternative products that could significantly reduce disease and death caused by cigarette smoking 14,15. Moreover, processes and/or platforms such as INTERVALS that encourage transparent sharing of data in a way that allows easy review and understanding should facilitate objective evaluation of the evidence 16.

Systems Toxicology

Systems toxicology and 21st century toxicology 17, aims to create a detailed understanding of the mechanisms by which biological systems respond to toxicants so that this understanding can be leveraged to assess the potential risk associated with chemicals, drugs, and consumer products.

Fig 2: Steps that define the Systems Toxicology maturity, from biological network models to dynamic adverse outcome pathway (AOP) models.
Systems toxicology aims to extrapolate short-term observations to long-term outcomes, and translate the potential risks identified from experimental systems to humans. Adapted from Figure 2 in 20.

Toxicity testing is at a turning point now that long-range strategic planning is in progress to update and improve testing procedures for potential stressors. The U.S. EPA commissioned the National Research Council to develop a vision for toxicity testing in the 21st century 6,18 to base the new toxicology primarily on Pathways of Toxicity 19. The report by the U.S. National Research Council envisions a shift away from traditional toxicity testing and toward a focused effort to explore and understand the signaling pathways perturbed by biologically active substances or their metabolites that have the potential to cause adverse health effects in humans. This understanding should allow researchers to:

  • achieve testing of broad coverage of chemicals, mixtures, outcomes and life stages;
  • significantly increase human relevance;
  • reduce the cost and time required to conduct chemical safety assessments; and
  • reduce and potentially eliminate high-dose animal testing.

The identification of PoTs is imperative in order to understand the Mode Of Action of a given stimulus and for grouping together different stimuli based on the toxicity pathways they perturb. The first component of the vision focuses on pathway identification, which is preferably derived from studies performed in human cells or cell lines using omics assays. The second component of the vision involves targeted testing of the identified pathways in whole animals and clinical samples to further explain toxicity pathway data. This two-component toxicity-testing paradigm, combined with chemical characterization and dose-response extrapolation, delivers a much broader understanding of the potential toxicity associated with a biologically active substance.

Systems biology plays an important role in this paradigm, consolidating large amounts of information that can be probed to reveal key cellular pathways perturbed by various stimuli 20.


Fig 3: Integrating classical toxicology with quantitative analysis of the molecular and functional changes induced by toxicants, systems toxicology relies on the latest technological developments in both experimental and computational sciences 20.

21st century toxicology has identified the promise of new technologies and the need for large-scale efforts. Aligned with the 3Rs strategy — animal-use should be reduced, refined, and replaced — in vitro studies using relevant test systems and systems biology approaches offer new prospects in the field of human toxicology 21.

Network-Based Approach to Systems Toxicology and Systems Biology Verification

With the unprecedented amounts of data accompanying various high throughput technologies, new computational approaches are developed to facilitate robust analysis and interpretation of these large datasets 22-24.

Unlike linear pathways, a network view can better explain biological effects of exposures. Biological networks consist of nodes that are biological entities and edges or links that are the interactions between the nodes in the network. Networks used for toxicological assessment can be built using existing data, after which the network structure can be verified with new experimental data 25. Biological network models can also be built using mechanistic information captured in the scientific literature that describe well-controlled experiments to elucidate causal relationships between biological entities. Such models can be made tissue- or disease- specific. One repository hosting such causal biological network models is the Causalbionet 26,27. The network models are scripted in the Biological Expression Language (BEL) that is easy for human to understand and that can be computed, owing to its controlled vocabularies for biological entities, functions, as well as context information (e.g. species, tissue type).

In addition to the network model backbone that describes the molecular interactions involved in a given biological process, the network models contain a downstream layer consisting of mRNA nodes that are linked to certain model backbone nodes with causal edges. These edges depict known gene expression regulation by the biological entity in the model backbone. Without imposing a statistical significance threshold, regulated genes (based on fold change) in a gene expression experiment are mapped to the network model downstream layer. This allows the inference of the activity of several nodes in the model backbone. The scoring algorithm takes then into account the network model topology, and computes the perturbation amplitude of the network as a whole. Such score can be aggregated across relevant networks to give an overall biological impact factor.

To learn more about the systems toxicology approach using causal models, watch this video.

Fig 4: A five-step approach to quantitative mechanism-based systems impact assessment (adapted from 19).
(1) design of the experiment to support robust data production. Study design is dependent on the test system, the test item to be studied, the exposure type, and the endpoints that need to be measured to answer specific question(s)
(2) compute systems response profiles from the -omics data to summarize the effect of the test item/exposure on the test system, as compared to a negative control
(3) biology relevant to the test system is consolidated from the scientific literature and public datasets in the form of causal networks
(4) network perturbation amplitude scores are calculated using the gene expression data and causal networks. These scores reflect the level of perturbation of the test item/exposure for a set of specific biological pathways/networks, as compared with the negative control
(5) product biological impact is calculated based on the aggregation of individual network perturbation amplitude scores

Questions arise as to how we can best manage the uncertainties inherent in the application of systems biology information to safety testing. Specifically, how can one ensure the validity of systems biology-based approaches and the resulting information? With a goal to maintain scrutiny in data analysis and interpretation, we have recently proposed a systems biology verification process and a methodology for verifying the output of research processes in industry 28,29.

References

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  2. World Health Organization. The scientific basis of tobacco product regulation. Report of a WHO Study Group (TobReg). WHO Technical Report Series. no. 945 (2007)
  3. Zeller, M., Hatsukami, D. & Strategic Dialogue on Tobacco Harm Reduction, G. The Strategic Dialogue on Tobacco Harm Reduction: a vision and blueprint for action in the US. Tobacco control 18, 324-332 (2009)
  4. Smith, M. R. et al. Evaluation of the Tobacco Heating System 2.2. Part 1: Description of the system and the scientific assessment program. Regulatory toxicology and pharmacology : RTP (2016)
  5. Morven Dialogues. Core Principles Concerning the Implementation of Effective and Workable Tobacco, Nicotine, and Alternative Products Policies for Reducing Disease and Death from Tobacco Use. (2015)
  6. Begley, C. G. et al. Reproducibility in science: improving the standard for basic and preclinical research. Circulation research 116, 116-126 (2015)
  7. Couchman, J. R. Peer review and reproducibility. Crisis or time for course correction? J Histochem Cytochem 62, 9-10 (2014)
  8. Drubin, D. G. Great science inspires us to tackle the issue of data reproducibility. Mol Biol Cell 26, 3679-3680 (2015)
  9. Frye, S. V. et al. Tackling reproducibility in academic preclinical drug discovery. Nat Rev Drug Discov 14, 733-734 (2015)
  10. Gaudart, J. et al. Reproducibility issues in science, is P value really the only answer? Proceedings of the National Academy of Sciences of the United States of America 111, E1934 (2014)
  11. Iorns, E. et al. New forms of checks and balances are needed to improve research integrity. F1000Research 3, 119 (2014)
  12. Nature Editorial. Announcement: Reducing our irreproducibility. Nature 496, 398 (2013)
  13. McNutt, M. Journals unite for reproducibility. Science 346, 679 (2014)
  14. Kozlowski, L. T. et al. Obsolete tobacco control themes can be hazardous to public health: the need for updating views on absolute product risks and harm reduction. BMC public health 16, 432 (2016)
  15. Morven Dialogues. Core Principles Concerning the Implementation of Effective and Workable Tobacco, Nicotine, and Alternative Products Policies for Reducing Disease and Death from Tobacco Use. (2015)
  16. Carlo, G. L. et al. The interplay of science, values, and experiences among scientists asked to evaluate the hazards of dioxin, radon, and environmental tobacco smoke. Risk analysis : an official publication of the Society for Risk Analysis , 37-43 (1992)
  17. Hartung, T. Lessons learned from alternative methods and their validation for a new toxicology in the 21st century. Journal of toxicology and environmental health. Part B, Critical reviews 13, 277-290 (2010)
  18. Testing NRCCoT and Agents AoE. Toxicity testing in the 21st century: A vision and a strategy. National Academy Press (2007)
  19. Basketter, D. A. et al. A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing - t4 report. Altex 29, 3-91 (2012)
  20. Sturla, S. J. et al. Systems Toxicology: From Basic Research to Risk Assessment, Chem Res Toxicol 27(3),314-329 (2014)
  21. Daneshian, M. et al. A framework program for the teaching of alternative methods (replacement, reduction, refinement) to animal experimentation. Altex 28, 341-352 (2011)
  22. Hoeng, J. et al. A network-based approach to quantifying the impact of biologically active substances. Drug discovery today 17, 413-418 (2012)
  23. Martin, F. et al. Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks. BMC systems biology 6, 54 (2012)
  24. Martin, F. et al. Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models. BMC bioinformatics 15, 238 (2014)
  25. Zhang, W. Network toxicology: A new science. Computational Ecology and Software 6, 31 (2016)
  26. Boué, S. et al. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database 2015, bav030 (2015)
  27. Talikka, M. et al. Causal Biological Network Database: A Comprehensive Platform of Causal Biological Network Models Focused on the Pulmonary and Vascular Systems. Computational Systems Toxicology, 65-93 (2015)
  28. Meyer, P et al. Industrial methodology for process verification in research (IMPROVER): toward systems biology verification. Bioinformatics 28(9), 1193-1201 (2012)
  29. Meyer, P. et al. Verification of systems biology research in the age of collaborative competition. Nature biotechnology 29, 811-815 (2011)

⚑ Reduced-Risk Products (“RRPs”) is the term we use to refer to products that present, are likely to present, or have the potential to present less risk of harm to smokers who switch to these products versus continued smoking. We have a range of RRPs in various stages of development, scientific assessment and commercialization. Because our RRPs do not burn tobacco, they produce far lower quantities of harmful and potentially harmful compounds than found in cigarette smoke.