Throughout the strategic planning process, a number of cross-cutting themes emerged that are relevant across multiple goals and objectives. NIDA will work to ensure that these themes are addressed across institute programs and initiatives. These cross-cutting elements include:
- advancing basic research on neuroscience and biology
- leveraging technology
- driving innovation
- increasing scientific rigor and reproducibility
- building a strong, diverse, multidisciplinary scientific workforce
- promoting collaboration
- encouraging data and resource sharing (data harmonization)
- supporting health equality
- increasing the real-world relevance of research (translation)
Advancing fundamental knowledge of basic biological, and especially neurobiological, processes is critical for advancing our understanding of the effects of drugs and in guiding the design of interventions to prevent and treat substance use disorders (SUDs). SUDs are complex disorders involving disruption of brain circuits involved in reward, decision-making, learning, and self-control. They are mediated by complex biological, social, environmental, and developmental factors that dynamically interact to influence risk, trajectory, and outcomes. Understanding this complexity will require drawing upon multiple disciplines across biomedicine, including neuroscience, genetics/epigenetics, behavioral and social sciences, development research, and information sciences.
Beyond Simply DNA: Epigenetics
It has become increasingly clear in the past two decades that the roots of inheritance extend beyond the genome and its simple four-letter code. A top priority across all biomedical research disciplines is studying an additional form of inheritance known as epigenetics, so-named because it operates on top of (epi, in Greek) simple genetics, affecting which genes actually get expressed. Its mechanisms remain a complex puzzle that many scientists are working hard to piece together.
We now know that the epigenetic changes affecting cellular patterns of gene expression are a direct response to and reflection of that cell’s history. The epigenome is constantly changing in response to signals coming from inside the cell, from neighboring cells, or from the outside world. In this way, the specific modifications along the DNA molecule or on the proteins that package it (i.e., histones) constitute a kind of cellular memory of a person’s experiences, good and bad, throughout his or her lifetime.
Importantly, these epigenetic marks can be not only mapped and characterized but also manipulated to better understand and, in theory, treat various disorders, including addiction.
The long-lasting nature of the behavioral changes seen in SUDs suggests that changes in patterns of gene expression, like those resulting from epigenetic modifications, are occurring in the brain. Independent lines of research have clearly shown that these processes play a crucial role in mediating the lasting effects of drugs on the brain. For instance, we now have robust evidence—albeit mostly from animal studies—that repeated exposure to drugs of abuse can induce changes in the brain's reward regions through various modes of epigenetic regulation. Furthermore, in some cases, researchers have been able to demonstrate a direct link between those epigenetic changes, gene expression, and addiction-related behavioral problems.119
Studies of epigenetic mechanisms in addiction are providing an unprecedented view of the range of genes and noncoding regions of DNA that are affected by repeated drug exposure and the precise molecular basis of that effect. Exciting new research is being conducted to validate key aspects of this work in human addiction and evaluate whether we can mine this information to develop new diagnostic tests and more effective treatments for SUDs.
The last few years have seen tremendous advances in the development and implementation of technologies that have great promise for accelerating research on drug use and addiction. Particularly prominent are technologies for gene sequencing, epigenetic analyses, neuronal cell classification, brain imaging, and modulation of brain circuits. Also relevant is the expanding access to increasingly larger databases of genetic, epigenetic, transcriptomic, and clinical health data—through electronic health records and mobile health technologies—along with rapid advances in analytic, computational, and information technologies. Programs such as the BRAIN Initiative, the NIH Blueprint, and the NIH Common Fund are helping to drive accelerated technology development. NIDA will actively follow these advances and look for opportunities to capitalize on these developments to advance research on drug use and addiction.
The biomedical research workforce in this country includes a tremendous number of talented and dedicated scientists with innovative ideas for how to advance research. NIDA will work to encourage and reward innovation to drive advances in addiction research by (1) promoting interdisciplinary collaborations, (2) encouraging research and development through our small business innovation research program, (3) crowdsourcing the development of novel technologies and solutions through challenge grants, (4) supporting innovative researchers through novel mechanisms including our Avenir Awards Program, and (5) supporting training in cutting-edge areas important for driving innovation (e.g., data science).
Reliable and reproducible research findings are essential to the advancement of science. Over the last few years, multiple studies have reported a troubling lack of reproducibility of biomedical research findings.120-122 Though part of this lack of reproducibility could reflect biological diversity, other factors that are likely to contribute include: selective reporting of data, invalid statistical methods, substandard laboratory techniques, insufficient transparency in reporting key methodologies and findings, and a failure to train students in ethical scientific practices. NIDA is committed to the responsible stewardship of public funds and will focus on enhancing the reliability of the research we support. To this end, NIDA will actively contribute to the NIH-wide Rigor and Reproducibility Initiative, participate in relevant activities of scientific organizations focused on enhancing reproducibility through education and outreach, and make concentrated efforts to improve both the quality and credibility of addiction research.
The entire biomedical research enterprise relies on the creativity, innovation, and dedication of the Nation’s scientific workforce. NIH and NIDA are committed to supporting a sustainable and robust workforce of neuroscientists, clinicians, chemists, physicists, behavioral and social scientists, bioengineers, statisticians, economists, mathematicians, health services researchers, and others who are equipped to address the greatest challenges and opportunities in biomedical research. Attracting and retaining a diverse, well-trained, and multidisciplinary workforce is a key to achieving our overall mission and addressing the challenges of addiction research. Efforts will focus on increasing the number of scientists with the multidisciplinary training necessary to address the complexities of addiction research; supporting the development of a high-quality, diverse, and sustainable scientific workforce; enhancing recruitment and mentoring of underrepresented investigators; and improving mentoring of young scientists.
Fulfilling NIDA’s mission will require effective partnerships with and between stakeholders throughout the community, including collaborations between scientists, health care providers, engineers, informaticists, health care payers, pharmaceutical and biotechnology companies, public health organizations, patients and families, people in recovery, community prevention organizations, educators, federal and state agencies, and others. NIDA will facilitate collaboration among researchers in disparate fields and between researchers and the community. In addition, NIDA will work to maximize the impact of our research by working directly with diverse stakeholders to improve dissemination of NIDA research findings, support more rapid uptake of evidence-based practices, facilitate critical connections among areas of research, improve translation of basic findings to clinical interventions, and drive evidence-based decision-making across the community.
Data sharing is an essential element of applying the power of data science and information technology (Big Data) for SUD research. Harnessing large quantities of data generated by researchers across the world has numerous methodological and economic advantages and provides tremendous opportunities for gaining new insight into addiction. To realize this benefit, however, there are many challenges to overcome. In particular, scientists and users from diverse areas need to be able to find data easily and to analyze them in new ways. Combining data from various sources and formats requires implementation of data standards as much as possible, which can be achieved via usage of common data elements, shared ontologies, and data dictionaries. Operational challenges related to data curation, development of advanced analysis tools (including machine learning and artificial intelligence techniques) and visualization strategies, and establishment of a culture of data sharing and open access within the scientific community need to be addressed. NIDA will work to develop standard practices and approaches that create incentives for sharing data and for secondary analysis of existing data sets.
Rapid progress in addiction research over the past few years has been fueled in part by technological advances, particularly next-generation sequencing, genomics, epigenetics, epidemiology, and neuroimaging. These advances have enhanced our understanding of the biological, developmental, and environmental factors that affect brain function in health and disease. Big Data is now providing unprecedented new opportunities to maximize the value of research results, giving researchers the ability to analyze huge amounts of data in new ways—turning vast data sets of complex information into knowledge.
Big Data is more than just large data sets; it refers to the challenges and opportunities presented by combining and analyzing different types of complex data in an integrated way. In research on SUDs, these data sources include imaging, phenotypic, molecular, exposure, clinical, health, behavioral, and many other types of data generated by researchers, hospitals, and mobile devices around the world. These data have the power to reveal new treatments, determine the genetic and environmental causes of SUDs, support the development of precision medicine in health care, and so much more.
There are certainly challenges to overcome, such as:
- how and where to store massive data sets
- how to integrate and harmonize data
- how to ensure data quality and consistency
- how to facilitate efficient use of available data
- how to foster a culture of open science and data sharing
- how to ensure privacy and confidentiality
NIDA and the NIH Big Data to Knowledge program are committed to addressing these challenges and helping the biomedical research community harness Big Data’s full potential.
NIDA is committed to addressing health disparities, studying unique SUD issues of underrepresented populations, and supporting health equality research across the lifespan. This requires considering the impact of age, sex, sexual and gender identity, race, ethnicity, culture, and socioeconomic status on substance use and SUDs. NIDA will work to ensure that these factors are adequately addressed in the research we support.
Improving Outcomes by Considering Sex and Gender Differences
Historically, females have been underrepresented in scientific research. For instance, preclinical research often studies only male animals and cell lines. It is often assumed that results can be generalized to females, but this assumption is faulty. The same is true of human research and includes the field of addiction.123 At NIDA, research across all program divisions is showing that outcomes are not always the same in males and females. Some are stronger in one sex than the other, some occur only in one sex, and still other outcomes are opposite between the sexes.
There is a large body of SUD prevention and treatment research reporting outcomes that vary by sex. It is important to understand the mechanisms that underlie these differences to develop interventions targeted to males and females, which can improve outcomes in both sexes. Beyond the genetic differences between males and females, other mechanisms may include cultural, psychosocial, and environmental factors; sexual dimorphisms in the brain; and epigenetics.
NIDA will continue to support research targeted to understanding sex differences in both animal models and clinical research. This research includes the role of menstrual cycles and their associated hormones, pregnancy and the postpartum period, and factors such as intimate partner violence and trauma that are experienced mostly by women.
As we enhance our research on sex differences, we must also consider the role of gender factors—roles and expectations of males and females that are culturally and socially based. With respect to drug abuse, such factors can be reflected in differential access to drugs, reasons for using drugs, and access and barriers to treatment.
Sex is the most fundamental genetic difference; every cell has a sex. From this perspective, sex differences are the low-hanging fruit for achieving precision medicine. In June 2015, the NIH announced a new requirement to report data for both males and females in all human and vertebrate animal research effective January 25, 2016.124 This represents a crucial cultural shift, which will greatly improve the health of both women and men, including those with SUDs.
NIDA is committed to making our sponsored research more relevant and applicable to real-world settings by proactively tackling relatively neglected and challenging issues such as polydrug use (which will require the development of validated animal models), nontreatment-seeking populations, patients with complexities (e.g., older adults; those who are incarcerated, pregnant, or in the military; and those with psychiatric and physical health comorbidities), and the impact of social factors (e.g., poverty, racism, housing and educational inequality, etc.).