Drug-Abuse Nanotechnology - American Chemical Society

Perspective Cite This: ACS Chem. Neurosci. XXXX, XXX, XXX−XXX

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Drug-Abuse Nanotechnology: Opportunities and Challenges Morteza Mahmoudi,*,† Sepideh Pakpour,‡,§ and George Perry∥ †

Department of Anesthesiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States ‡ Infectious Disease & Microbiome, Broad Institute, Cambridge, Massachusetts 02142, United States § School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada ∥ Neurosciences Institute and Department of Biology, College of Sciences, University of Texas, San Antonio, Texas 78249, United States ABSTRACT: Opioid drug abuse and dependence/addiction are complex disorders regulated by a wide range of interacting networks of genes and pathways that control a variety of phenotypes. Although the field has been extensively progressed since the birth of the National Institute on Drug Abuse in 1974, the fundamental knowledge and involved mechanisms that lead to drug dependence/addiction are poorly understood, and thus, there has been limited success in the prevention of drug addiction and development of therapeutics for definitive treatment and cure of addiction disease. The lack of success in both identification of addiction in at-risk populations and the development of efficient drugs has resulted in a serious social and economic burden from opioid drug abuse with global increasing rate of mortality from drug overdoses. This perspective aims to draw the attention of scientists to the potential role of nanotechnologies, which might pave the way for the development of more practical platforms for either drug development or identification and screening of patients who may be vulnerable to addiction after using opioid drugs. KEYWORDS: Opioid drug abuse, dependence/addiction, nanoparticle, nanotechnology, early detection, therapeutics

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materials are available online11). For example, urine drug monitoring using immunoassay and/or chemotherapy has a range of shortcomings, including the possibility of false result readouts based on detection limitation (e.g., inability of immunoassay to sensitively detect oxycodone, methadone, and fentanyl; inability of chemotherapy to detect morphine before codeine metabolism) and concealment (e.g., by taking poppy seeds).7−10,12,13 Although there were several attempts to enhance the sensitivity and specificity of these assays (to μg/ mL level) (e.g., development of surface plasmon resonance based immunosensors14), the field urgently needs alternative platforms for fast and accurate identification of drugs with robust specificity and sensitivity. Apart from identification of the at-risk population, treatment of the addiction disorders is another major issue in the field of drug abuse. One of the central reasons that the current approaches have not been successful is the numerous and extremely complicated strategies and alterations that host systems use to cope with drugs. These strategies/alterations

INTRODUCTION Opioid abuse remains a serious social and economic burden with an estimated 64 000 deaths from drug overdoses (including both illicit drugs and prescription opioids) in the United States in 2016, which has nearly doubled in a decade, according to the National Center for Health Statistics.1 Drug abuse and dependence/addiction are complex disorders that are regulated by a wide range of interacting networks of genes and pathways that control a variety of phenotypes.2−6 Therefore, both identification of the at-risk population and treatment of the addiction disorders are strongly reliant on the development of new and innovative approaches for understanding the mechanisms underlying drug dependency and addiction. For example, many of the current approaches to identify at-risk and addiction disorder populations are focused on screening questionnaires and urine drug monitoring.7,8 However, both sensitivity and specificity of these approaches together with the required time of detection in urine are below the clinical needs (e.g., typical cutoff for opioid drugs is ∼300−2000 mg/mL and the timing is 2−4 days).9,10 In other words, the conventional approaches have their own limitations which are mainly focused on long detection time (2−4 days), low sensitivity and specificity (∼300−2000 mg/mL), and false outcomes (detailed information on pros and cons of screening tools and resource © XXXX American Chemical Society

Special Issue: DARK Classics in Chemical Neuroscience Received: March 18, 2018 Accepted: May 7, 2018

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DOI: 10.1021/acschemneuro.8b00127 ACS Chem. Neurosci. XXXX, XXX, XXX−XXX

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ACS Chemical Neuroscience

Figure 1. (A) Gold nanorods substantially facilitate transmigration of siRNA across the blood brain barrier model (upper and lower chambers are representative of blood and brain ends, respectively), compared to the siRNA alone. (B) Substantially higher gene silencing efficiency of gold nanorod-DARPP-32 siRNA complexes in dopaminergic neuronal cells, compared to siRNA alone and conventional gene transfection approach (positive control). Reprinted with permission from ref 32 Copyright (2009) National Academy of Sciences.

(i) identification of at-risk individuals in the population (presently, it is impossible to predict which patients will be affected by opioids), (ii) new therapeutic targets, and (iii) personalized selection of appropriate treatments. Although probing the differences in genes of interest between addicted patients and healthy subjects can provide useful information on possible involved mechanism, dissecting out the roles of specific genes in addiction is a very difficult task that requires parsing the contributions of individual genes and complex gene−gene interactions. This aspect is further complicated by the differences between opioid dependency (physical dependence in which the body adapts to the drug) and addiction (i.e., an inability to stop using a drug; failure to meet work, social, or family obligations).

take place in different biosystems and organs, including brain anatomical15 and physiological16 functions, gut microbiota dysbiosis,17 immune system,18,19 and biological barriers20 (e.g., blood brain barrier). Therefore, it is obvious that a wide range of expertise (e.g., medicine, microbiology, physiology, genetics, bioengineering, nanomedicine, bioinformatics, mathematics, proteomics, and biochemistry) should be combined and interconnected to define and connect the involved mechanisms of drug dependency and addiction. Many experts already exist in all of those fields, but it is crucial to bridge the gap between these experts. The outcome may pave the way for the development of platforms which allows us to overcome the serious social and economic burden of opioid drug abuse safely, effectively, and at a lower cost. The main aim of this perspective is to introduce possible potential capacities of nanotechnology and shine more light by providing knowledge to the field of drug dependency and addiction. Due to their unique properties (e.g., physical, chemical, electrical, optical, and mechanical), nanotechnologies offer enormous opportunities in various fields of science, including medicine.21 For example, nanotechnologies are being widely used for targeted delivery of therapeutic biomolecules,22 contrast agents to monitor cancer cells and tumor binderies,23 hyperthermia,24 immunotherapies,25 and tissue engineering applications.26 Although nanotechnologies are now being used in identification and treatment of several diseases, including cancers,27 cardiovascular,28 and neurodegenerative disorders,29 their potential application in drug abuse is poorly investigated. For example, nanotechnologies have been widely used for drug delivery applications;22,30,31 however, their potential role in drug screening purposes, in various types of biological fluids including blood and urine, and also in drug-abuse applications is poorly investigated. In other words, there are fewer than expected reports on developing nanotechnologies for the treatment of drug abuse,32−35 detecting drugs (e.g., methamphetamine) in human urine36−38 and saliva,1,39,40 reducing the toxic concentrations of drugs in blood,41 and also for probing the effect of drug abuse on biochemical or structural variations of brain tissue.42 Developing new and innovative approaches for understanding the mechanisms underlying addiction is of crucial importance in the field of opioid drug abuse, as it may lead to

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NANOTECHNOLOGIES FOR DRUG ADDICTION THERAPY Nanotechnologies can be designed and engineered to regulate brain signaling pathways that are associated with drug addiction. This particular approach needs development of multifunctional nanoparticles that have a unique capacity to transmigrate across blood brain barrier and target the desired site of the brain.43 One of the well-recognized opioiddependent triggering biomolecules is adenosine 3′,5′-monophosphate-regulated phosphoprotein (DARPP-32).44,45 This biomolecule has crucial effects on extracellular signal-regulated kinase activity, which controls transcriptional and behavioral effects of substance abuse.32,46 For suppression of DARPP-32 gene expression, Bonoiu et al.32 developed gold nanorodDARPP-32 siRNA complexes with unique capacity to pass the blood brain barrier, target the dopaminergic neuronal cells, and suppress the target gene DARPP-32 (Figure 1). These surprising outcomes demonstrate the capacity of the gold nanorods for treatment of drug addiction. Law and coworkers47 used PEGylated quantum rod for the delivery of the same siRNAs to the dopaminergic neuronal cells, and their outcomes were in agreement with the previous report. Another developed approach was to conjugate glial cell linederived neurotrophic factor to the surface of nanoparticles to make drug-conjugated nanoparticles for treatment of cocaine addiction.33 The therapeutic efficacy of the conjugated nanoparticles was probed in cocaine addicted rat model, and B


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Due to their high surface to volume ratio, nanoparticles have a unique capacity in concentrating small biomolecules at their surfaces (i.e., the biomolecular corona). This unique feature of nanoparticles can be empowered by the use of targeting moieties at the surface of nanoparticles that have a great affinity to the desired drugs in urine. Using plasmonic nanoparticles, colorimetric assays could be developed for a fast, cheap, and reliable assessment of drugs or their related biomolecules (e.g., substance drugs and neurotransmitters) in urine. For example, nanoparticle-based paper devices were developed to detect traces of cocaine in human saliva samples (Figure 2).59 This

the results revealed that the nanoparticles could not only block the acquisition of cocaine self-administration but also decreased lever cocaine dose response compared to the control groups. Few other nanotechnologies were also developed to reduce the adverse effects of substance abuse on other organs; for example, hydrophilic C60(OH)10/2-hydroxypropyl-β-cyclodextrin nanoparticles were developed to minimize adverse effects of acetaminophen’s overdose on a liver injury.48

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NANOTECHNOLOGIES FOR BETTER UNDERSTANDING OF BRAIN ANATOMICAL CHANGES Opioid drugs demonstrated a great capacity in altering important brain areas, including the cerebral cortex and limbic system.49−51 These anatomic functional variations can alter biochemistry balance of the brain (e.g., substantial changes in release of neurotransmitters such as dopamine, serotonin, gamma-aminobutyric acid, and norepinephrine) and affect many of the brain’s critical functions, including problem solving, decision making, and rewarding circuit system.15,52,53 Real time monitoring of particular neurotransmitters and brain anatomical changes using highly sensitive approaches is of great importance to shed more light on the mechanistic role of drug abuse on brain functions. Magnetic nanoparticles, as sensitive contrast agents, have been used for substantial enhancement of diagnostic capacity of magnetic resonance imaging (MRI) in animal and human models.24 Among various types of nanoparticles, superparamagnetic iron oxide nanoparticles showed great potential in safe and efficient molecular imaging of different sites of the human body, including brain tissue.54,55 These nanoparticles demonstrated promising outcomes for a wide range of theragnosis purposes in a wide range of neurodegenerative diseases, including multiple sclerosis, stroke, Alzheimer’s disease, meningitis, epilepsy, and brain tumors (see ref 56 for a comprehensive review on in vivo molecular imaging capacity of disease particles). Magnetic nanoparticles have been also used for substance abuse.57,58 For example, Sagar and coworkers developed iron oxide nanoformulation of a highly selective and potent morphine antagonist, which could protect modulation of neuronal dendrite and spine morphology during both morphine exposure and morphine-treated HIV infection.58 The use of engineered superparamagnetic iron oxide nanoparticles for imaging of anatomic functional and biochemistry variations in addicted patients may open a new opportunity to provide better information regarding the nature of brain structural and biochemical changes and site of changes after drug addiction. Despite the critical potential of nanotechnologies, their usage in the field of drug abuse has been poorly evaluated.

Figure 2. Cartoon showing the mechanism for detection of cocaine using a nanoparticle-based paper device. The presence of cocaine can reassemble anticocaine aptamer fragments (i.e., ACA-1 and ACA-2) and quench the UCNPs luminescence by gold nanoparticles, which can be identified and roughly quantified by a smart phone camera. Reprinted with permission from ref 59 He et al. Copyright 2016 American Chemical Society.

device contains an anticocaine aptamer (ACA), which demonstrated high affinity to cocaine, upconversion nanoparticles (UCNPs), and gold nanoparticles; the aptamer was cut into two flexible pieces (ACA-1 and ACA-2). To attach nanoparticles and aptamers to the cellulose filter of the device, the UCNPs were functionalized with poly(ethylenimine) (PEI), and ACA-1 was directly modified with amine. ACA-2 was modified with sulfhydryl and attached to the gold nanoparticles. In the absence of cocaine, there are no interactions between ACA-1 and ACA-2 and the luminescence of UCNPs; by contrast, the existence of cocaine can reassemble ACA-1 and ACA-2 and quench the UCNPs luminescence by gold nanoparticles, which can be identified and roughly quantified. A similar strategy can be used for other types of substance abuse. The use of engineered nanoparticles can substantially enhance identification and discrimination capacity of healthy, at-risk populations and addiction patients in a short period of time with reliable and reproducible outcomes. Although nanotechnologies showed great potential in overcoming the issues of conventional drug monitoring approaches, their usage for this purpose is poorly investigated.

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NANOTECHNOLOGIES ENHANCE SENSITIVITY OF DRUG DETECTION APPROACHES Many of the current approaches to identify at-risk populations and addiction patients are focused on screening questionnaires and urine drug monitoring.7,8 Although promising, the urine drug monitoring conventional approaches are time-consuming and faced a wide range of issues including, but not limited to, low sensitivity (∼300−2000 mg/mL) and unacceptable risk of false outcomes.9,10 The central reason for the low sensitivity of these approaches is the low concentration of drugs in the urine.

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MICROBIOME-GUT-BRAIN AXIS Human gut is colonized with a plethora of microorganisms, playing crucial roles in physiology and immunity and impacting human health and disease.60−63 Although a high interindividual variability exists in the gut microbiome, an individual’s gut microbiota can remain stable over time.64 Many factors such as antibiotic treatments can change the gut microbial structure C


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is shown to induce multiple gastrointestinal symptoms (e.g., impairing epithelial integrity) which cause release of several gut microbiota biomolecules into circulation [it may even cause bacterial translocation, which may induce immune reaction80 and in some cases induce sepsis81,82 (e.g., chronic administration of morphine activated Pseudomonas aeruginosa virulence expression which led to lethal gut-derived sepsis in mice model83)].84−86 While proteomic characterization of blood plasma is of central importance to possible biomarker discovery studies, the vast dynamic range and high complexity of the plasma proteome presents serious challenges, which have so far led to unacceptable trade-offs between depth of coverage and sample throughput.87 Therefore, advances in the fields of proteomics and genomics have not yet translated into a workable platform for either the development of suitable drugs to identify at-risk populations or diminishing the risk of addictive relapse. This is largely due to the fact that no individual plasma protein is specific and sensitive enough to identify the source of its corresponding genetic variation and gene−gene interactions, and appropriate technology platforms for accurate and sensitive multivariate plasma analysis relevant to opioid drug addiction have remained elusive. How Can Nanotechnologies Be of Help? Nanotechnologies have a unique capacity to be used as a probe for gathering useful information on dissimilarities between the plasma composition of healthy and addicted patient groups. Once in contact with human plasma or serum, the surface of nanoparticles is covered by a layer composed of different type of biomolecules (i.e., biomolecular corona).88−90 The biomolecular corona contains wide range of proteins, enzymes, and other biomolecules such as metabolites. During the formation of biomolecular corona, proteins with high concentrations first come to the surface of nanoparticles. However, these proteins will be replaced by other proteins with a higher binding affinity to the surface of nanoparticles, which is known as the Vroman effect.91,92 This means that the biomolecular corona provides more information about proteins that are at very low concentrations in human plasma and other complex biological fluids; without biomolecular corona, such low concentration proteins cannot be easily detected and analyzed in the entire plasma using current available proteomics approaches.93,94 The composition of biomolecular corona is strongly dependent on the physicochemical properties of nanoparticles,95 environmental factors (e.g., incubating temperature96), and biological factors (e.g., type of biological fluids97 and plasma/cell sex98).99 Recently, it was found that various diseases and medical interventions may change the concentration/conformation of human plasma proteins and metabolomes; these include various types of cancers, hemodialysis, neurodegenerative diseases, and diabetes.100−105 Changes in plasma protein profiles/biomarkers are already widely used for diagnosis of several diseases.106−110 On the basis of these variations, we introduced the concept of disease-specific protein corona and personalized protein corona, demonstrating that variation in plasma proteins caused by disease type can significantly change the composition of the biomolecular corona formed around NPs.111−114 The concept is now being increasingly accepted by the scientific community, and many other groups have applied this strategy to define the exact biological identity of NPs for safe design of nanotechnologies.115−120 In addition, very recently, we demonstrated that the

and distance it from a stable and healthy ecosystem. The unbalanced gut microbiota may be associated with several diseases65 such as neurodegenerative disorders.66−68 The gut microbiome can influence monoamine-containing enterochromaffin cells (residing in the mucosal and submucosa of the stomach and small intestine) and their ability to produce serotonin, dopamine, and norepinephrine, which can affect host behavior.69 Many reports have revealed this bidirectional communication between gut microbiota and neural signaling, which is referred to as the microbiome-gut-brain axis.49,54,61−65 Inflammatory gut microbial species have a direct connection with the brain via the neurotransmitters they produce and the vagus nerve receptors in the gut.70 The vagus nerve seems to be capable of differentiating between harmless and potentially harmful bacteria, even when there is no inflammation, and vagal pathways can send signals to the brain that can induce either anxiety-producing or calming effects. The gut-brain axis with serotonin functioning has been found to be a key signaling molecule in the enteric nervous system (ENS) and the central nervous system (CNS).71 The gut microbiota can directly or indirectly recruit tryptophan metabolism and serotonergic signaling and profoundly influence the CNS.72 Chronic opioid usage can lead to gut microbiome dysbiosis. Morphine, for example, can disrupt the gut epithelial barrier and lead to a leaky gut and bacterial translocation. Changes in the gut microbiota can also affect opioid tolerance in the cell bodies of extrinsic sensory neurons.73 The microbiome can also play a role in the development of opioid tolerance73 and alter important synaptic transcripts in the brain’s reward circuitry.17 Gut microbiota depletion can increase sensitivity to the rewarding and sensitizing properties of cocaine,17 which may be through the reduction of short chain fatty acids (SCFAs) production.17 The relationship between gut bacteria and drug addiction is still in its infancy, and in-depth studies are required to better understand and define the mechanisms involved in opioid addiction and dependency.

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POTENTIAL APPLICATION OF NANOTECHNOLOGIES IN MONITORING BLOOD PLASMA VARIATIONS Gut-Brain Axis and Its Impact on Blood Plasma. Opiate addiction and dependency is involved in the activation of several signaling pathways [e.g., cAMP-dependent pathway (i.e., adenylyl cyclase pathway) and extracellular signal-regulated kinase (ERK) mitogen-activated protein (MAP) kinase cascades],46,74,75 transcription factors, and proteins (FBJ murine osteosarcoma viral oncogene homologue B (FosB), cFos, activator protein 1 complexes, and cAMP-responseelement binding protein). These pathways are present and vary in different parts of brain tissue, spinal cord, and gut. For instance, cAMP pathways are upregulated in locus coeruleus, ventral tegmental area, periaqueductal gray, dorsal horn of the spinal cord, and myenteric plexus of the gut.74,76 Both the gut and brain may induce substantial variations to plasma composition due to substance abuse, which at least in theory can be detected by advanced proteomic approaches. For example, the link between gut microbiome and brain disorders can change immune functions and immune-related proteins/ paracrine-factors and cells.77,78 It is noteworthy that some of the immune related cells (e.g., B cells, T cells, and macrophages) express opioid receptors.79 Gut microbiota have displayed a great capacity in dynamic alteration of several circulating biomolecules of their host. Opioid substance abuse D


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the blood.133 As mentioned earlier, these variations in the metabolomes and biomolecular composition of plasma can make detectable substantial changes in biomolecular corona at the surface of nanoparticles. By combination of advanced mathematical approaches with the biomolecular corona, this approach may provide a unique capacity to (i) identify and discriminate vulnerable populations to addiction disorders after using opioid drugs, (ii) to identify patients vulnerable to addiction after recreational use of opioid drugs, (iii) to identify novel protein and metabolomic markers related to addictive behavior, and (iv) to investigate genes possibly helpful in identifying at-risk individuals. After more information is obtained on the important proteins which may have critical roles in drug dependency addiction, much easier, cheaper, and faster colorimetric nanotechnologies can be developed for diagnostic purposes (Figure 4). For

metabolomes can also significantly affect the composition of biomolecular corona and even can interfere with the Vorman effect.121 As drugs can substantially affect the metabolomics composition of blood plasma122,123 and can also interact with plasma proteins,124−126 we can expect these metabolomic and protein variations to alter the biomolecular corona profile of nanoparticles, which may be used for identification and discrimination of not only people with addiction disorder, but also drug-specific addictions. This is driven by previous reports on the use of nanotechnologies for detection of substance abuse using the specific metabolites of drugs in human sweat.127,128 For example, anticotinine antibody functionalized gold nanoparticles were developed to provide fast and accurate chemical information on specific metabolites of sweat.127 We believe that the changes in metabolites and plasma protein compositions that reflect the spectrum of healthy, atrisk population for drug dependency and drug addiction disorder can drastically change biomolecular corona composition (Figure 3). The central reason for the proposed substantial

Figure 4. Scheme showing possible role of substance abuse biomolecular corona in changing the color of colloidal nanoparticles which may be used for development of cheap, fast, and reliable biosensors for identification of substance abuse disorder.

example, plasmonic nanoparticles (e.g., gold) can be engineered in a way that the existence of particular protein, specific to drug dependency/addiction, can induce aggregation of the particles and cause visible substantial changes in the colloidal color of nanoparticle-plasma solutions. This approach has been successfully used for a wide range of biomolecular sensing134 and diseases such as cancer135 and infectious diseases.136 Another type of colorimetric approach has been employed for detection of cocaine. For instance, Du and coworkers137 combined cocaine aptamer fragments (i.e., SH-C2) labeled magnetic nanoparticles and DNAzyme-based colorimetric sensor (using hemin-G-quadruplex complex) for fast and sensitive (i.e., 50 nM) detection of cocaine in a 3,3,5,5tetramethylbenzidine sulfate (TMB)−H2O2 reaction system. The functionalized magnetic nanoparticles can concentrate and separate cocaine and, in combination with the catalytic activity of the DNAzyme in the reaction system, can change the solution color at the very low cocaine concentration (Figure 5). Importance of Multianalyte Strategies. The majority of the conducted studies in the field of drug abuse have been focused on monitoring the differences of selected genes between at-risk populations, addicted patients, and healthy individuals; however, due to parsing the contributions of individual genes and complexity of gene−gene interactions,

Figure 3. Scheme showing differences in the biomolecular corona structures after interaction of nanoparticles with plasma of healthy individuals and people with a risk of drug dependency and patients with drug addiction disorders. The changes in the biomolecular corona structures can be driven by variations of plasma protein and metabolomic compositions of healthy, at-risk, and drug addiction disorder individuals.

changes in the biomolecular corona, beside the interaction of drugs with metabolomes and proteins, is that it is increasingly being accepted that gut microbiota can generate numerous biomolecules that can enter the bloodstream and thus have a crucial capacity to directly alter the compositions of plasma metabolomes and also indirectly regulate protein conformations and hormones.129−131 In addition, the gut microbiota can signal to the brain via a number of pathways, including regulating metabolomic profiles of host plasma, immune activity, and the production of a wide range of proinflammatory cytokines, paracrine factors, and neurotransmitters which can directly and indirectly affect the activity of the host’s central nervous systems.132 Many of these produced biomolecules can cross the blood brain barrier and affect brain function, which may further induce inflammatory and metabolomic changes to E


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The multianalyte evaluations may be used for minimizing the complication in finding differences between opioid dependency and addiction. In other words, this approach may be able to robustly probe subtle dissimilarities in genetics, proteomics, and metabolomics between the plasma composition of healthy and addicted patient groups. Such a multianalyte analysis may define the role of proteins, genes, and metabolomes relevant to the identification of individuals who are addicted and distinguish variations among them according to the drug they are abusing. This may help in the discovery of novel genes and their interactions that can be easily affected by proteomic variations induced by drug abuse. It may also contribute to the discovery of new drugs that can act against important protein modifications in human plasma and thus inhibit drug addiction.

Figure 5. Scheme showing the color change strategy of the biosensor in the presence of cocaine. Reprinted with permission from ref 137, Du et al. Copyright 2011, Royal Society of Chemistry.

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NANOTECHNOLOGIES FOR ALTERATION OF GUT MICROBIOTA PATTERN As stated earlier, it is increasingly being accepted that the gut microbiota plays multiple critical roles in health spectrum maintenance of their host, including controlling gastrointestinal physiology, communication with central nervous system through microbiota-gut-brain axis, and alteration of plasma metabolomes and hormones.133,143 Therefore, it is legitimate to hypothesize that manipulation of gut microbiota profiles may have a capacity to minimize adverse effects of drug abuse. To develop such an approach for this purpose, we need a deep understanding of the role of drug abuse on the gut-brain axis; as our understanding of the effects of opioid drugs on the gutbrain axis is extremely limited, a huge amount of effort should be conducted to achieve the required information on the potential roles of bacterial pattern alteration on minimizing prevalence of drug addiction disorders. Initial efforts are now being conducted to change the gut microbiota pattern for therapeutic applications.144−149 As interest in manipulating the gut microbiota to treat diseases increases, nano- and micro-

dissecting out the roles of specific genes in at-risk population and addiction has proved to be very difficult. By combination of biomolecular corona and proteomics approaches, one promising possibility for achieving more in-depth understanding on the underlying mechanisms of drug dependency addiction may be the use of multianalyte (e.g., combination of proteomics, genetics, and metabolomics) approaches. Although the central disadvantage of multianalyte approaches is to deal with a large data source and multivariant analysis, these issues are now being solved by recent advances in data science approaches and advanced statistics and clustering approaches such as machine learning approach and deep learning techniques.138−141 For example, Cohen et al.142 developed a multianalyte blood test (by combination of protein biomarkers with genetic biomarkers) followed by machine learning evaluation of the outcomes for successful robust identification and discrimination of eight types of cancers (i.e., breast, colorectum, ovary, liver, lung, stomach, pancreas, and esophagus).

Figure 6. Engineering the gut microbiota to treat drug abuse: (A) conventional probiotic approach and (B) novel microbiome therapeutic approach using nano- and microtechnologies. F


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ACS Chemical Neuroscience technologies148−150 can innovatively be employed for target delivery of pre- and probiotics to specific sites of the gut and its mucous layer. Persistent establishment of these organisms can eventually drive the host gut microbiota to a healthy state and eventually impact the host’s health (Figure 6). So far, almost all of the reports in the field are focused on monitoring the toxic effects of antibacterial nanoparticles (e.g., silver nanoparticles) on gut microbiota and inducing dysbiosis.150−153 However, the use of nanotechnologies for controlling/altering gut microbiota pattern for therapeutic purposes is not being investigated.

(3) Hoehe, M. R., Köpke, K., Wendel, B., Rohde, K., Flachmeier, C., Kidd, K. K., Berrettini, W. H., and Church, G. M. (2000) Sequence variability and candidate gene analysis in complex disease: association of μ opioid receptor gene variation with substance dependence. Human molecular genetics 9, 2895−2908. (4) Crowley, J. J., Oslin, D. W., Patkar, A. A., Gottheil, E., DeMaria, P. A., Jr, O’brien, C. P., Berrettini, W. H., and Grice, D. E. (2003) A genetic association study of the mu opioid receptor and severe opioid dependence. Psychiatr. Genet. 13, 169−173. (5) Jin, J., Kittanakom, S., Wong, V., Reyes, B. A., Van Bockstaele, E. J., Stagljar, I., Berrettini, W., and Levenson, R. (2010) Interaction of the mu-opioid receptor with GPR177 (Wntless) inhibits Wnt secretion: potential implications for opioid dependence. BMC Neurosci. 11, 33. (6) Crist, R. C., Clarke, T.-K., Ang, A., Ambrose-Lanci, L. M., Lohoff, F. W., Saxon, A. J., Ling, W., Hillhouse, M. P., Bruce, R. D., Woody, G., and Berrettini, W. H. (2013) An intronic variant in OPRD1 predicts treatment outcome for opioid dependence in African-Americans. Neuropsychopharmacology 38, 2003. (7) Kahan, M., Srivastava, A., Wilson, L., Gourlay, D., and Midmer, D. (2006) Misuse of and dependence on opioids: study of chronic pain patients. Canadian Family Physician 52, 1081−1087. (8) Carmichael, A.-N., Morgan, L., and Del Fabbro, E. (2016) Identifying and assessing the risk of opioid abuse in patients with cancer: an integrative review. Substance abuse and rehabilitation 7, 71. (9) Gourlay, D. L., Heit, H. A., and Caplan, Y. H. (2010) Urine drug testing in clinical practice: the art and science of patient care; PharmaCom Group. (10) Christo, P. J., Manchikanti, L., Ruan, X., Bottros, M., Hansen, H., Solanki, D. R., Jordan, A. E., and Colson, J. (2011) Urine drug testing in chronic pain. Pain Physician 14, 123−143. (11) National Institute on Drug Abuse. Chart of Evidence-Based Screening Tools for Adults and Adolescents. https://www.drugabuse. gov/nidamed-medical-health-professionals/tool-resources-yourpractice/screening-assessment-drug-testing-resources/chart-evidencebased-screening-tools-adults. (12) Vaglienti, R. M., Huber, S. J., Noel, K. R., and Johnstone, R. E. (2003) Misuse of prescribed controlled substances defined by urinalysis. West Virginia medical journal 99, 67−70. (13) Katz, N. P., Sherburne, S., Beach, M., Rose, R. J., Vielguth, J., Bradley, J., and Fanciullo, G. J. (2003) Behavioral monitoring and urine toxicology testing in patients receiving long-term opioid therapy. Anesth. Analg. 97, 1097−1102. (14) Shankaran, D. R., Gobi, K. V., and Miura, N. (2007) Recent advancements in surface plasmon resonance immunosensors for detection of small molecules of biomedical, food and environmental interest. Sens. Actuators, B 121, 158−177. (15) Koob, G. F. (1992) Drugs of abuse: anatomy, pharmacology and function of reward pathways. Trends Pharmacol. Sci. 13, 177−184. (16) Robinson, T. E., and Berridge, K. C. (1993) The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res. Rev. 18, 247−291. (17) Kiraly, D. D., Walker, D. M., Calipari, E. S., Labonte, B., Issler, O., Pena, C. J., Ribeiro, E. A., Russo, S. J., and Nestler, E. J. (2016) Alterations of the host microbiome affect behavioral responses to cocaine. Sci. Rep. 6, 35455. (18) Sacerdote, P. (2006) Opioids and the immune system. Palliative medicine 20, 9−15. (19) Postler, T. S., and Ghosh, S. (2017) Understanding the holobiont: how microbial metabolites affect human health and shape the immune system. Cell Metab. 26, 110−130. (20) Fiala, M., Gan, X.-H., Zhang, L., House, S., Newton, T., Graves, M., Shapshak, P., Stins, M., Kim, K.-S., and Witte, M. (1998) Cocaine enhances monocyte migration across the blood-brain barrier. In Drugs of Abuse, Immunomodulation, and Aids, pp 199−205, Springer. (21) Kim, B. Y., Rutka, J. T., and Chan, W. C. (2010) Nanomedicine. N. Engl. J. Med. 363, 2434−2443. (22) Farokhzad, O. C., and Langer, R. (2009) Impact of nanotechnology on drug delivery. ACS Nano 3, 16−20.

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CONCLUSIONS AND FUTURE PERSPECTIVE This perspective offers an outline of key potential applications of nanotechnology in achieving not only more knowledge on drug dependency/addiction mechanisms but also efficient predictive and therapeutic approaches for opioid dependency and addiction diseases. Nanotechnologies are comprehensively being used/developed for diagnosis and treatment of a wide range of diseases; however, the field of drug-abuse nanotechnology has been poorly investigated. More specifically, although some efforts to use nanotechnologies were developed for diagnostic, drug detoxification, and therapeutic application in drug addiction here and there,32−34,41,47,48,57−59,137,154,155 no systematic trends can be drawn from the current literature. One of the central reason for the limited use of nanotechnologies in drug-abuse applications is the lack of investment from government and foundations in the field of drug-abuse nanotechnology. Therefore, we believe that specific funding for drug-abuse nanotechnology should be established and substantially increased to empower the use of nanotechnologies in the field, which may pave a way for emerging of breakthrough discoveries for diagnosis and therapeutic applications in drug abuse. With the help of funding agencies together with establishment of collaborations between nanomedicine and drug-abuse experts, we believe that nanotechnologies will provide a unique capacity for both predictive and therapeutic approaches in opioid dependency and addiction in the foreseeable future.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] or morteza. [email protected]. ORCID

Morteza Mahmoudi: 0000-0002-2575-9684 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS M.M. thanks Jonathan D. Pollock for his helpful comments and feedback. S.P. thanks the contribution provided by Ms. Negin Kazemian.

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REFERENCES

(1) Andreou, C., Hoonejani, M. R., Barmi, M. R., Moskovits, M., and Meinhart, C. D. (2013) Rapid detection of drugs of abuse in saliva using surface enhanced Raman spectroscopy and microfluidics. ACS Nano 7, 7157−7164. (2) Oslin, D. W., Berrettini, W., Kranzler, H. R., Pettinati, H., Gelernter, J., Volpicelli, J. R., and O’Brien, C. P. (2003) A functional polymorphism of the μ-opioid receptor gene is associated with naltrexone response in alcohol-dependent patients. Neuropsychopharmacology 28, 1546. G


Perspective

ACS Chemical Neuroscience (23) Brigger, I., Dubernet, C., and Couvreur, P. (2012) Nanoparticles in cancer therapy and diagnosis. Adv. Drug Delivery Rev. 64, 24−36. (24) Laurent, S., Dutz, S., Häfeli, U. O., and Mahmoudi, M. (2011) Magnetic fluid hyperthermia: focus on superparamagnetic iron oxide nanoparticles. Adv. Colloid Interface Sci. 166, 8−23. (25) Zanganeh, S., Hutter, G., Spitler, R., Lenkov, O., Mahmoudi, M., Shaw, A., Pajarinen, J. S., Nejadnik, H., Goodman, S., and Moseley, M. (2016) Iron oxide nanoparticles inhibit tumour growth by inducing pro-inflammatory macrophage polarization in tumour tissues. Nat. Nanotechnol. 11, 986. (26) Shi, J., Votruba, A. R., Farokhzad, O. C., and Langer, R. (2010) Nanotechnology in drug delivery and tissue engineering: from discovery to applications. Nano Lett. 10, 3223−3230. (27) Shi, J., Kantoff, P. W., Wooster, R., and Farokhzad, O. C. (2017) Cancer nanomedicine: progress, challenges and opportunities. Nat. Rev. Cancer 17, 20. (28) Mahmoudi, M., Yu, M., Serpooshan, V., Wu, J. C., Langer, R., Lee, R. T., Karp, J. M., and Farokhzad, O. C. (2017) Multiscale technologies for treatment of ischemic cardiomyopathy. Nat. Nanotechnol. 12, 845. (29) Hajipour, M. J., Santoso, M. R., Rezaee, F., Aghaverdi, H., Mahmoudi, M., and Perry, G. (2017) Advances in alzheimer’s diagnosis and therapy: The implications of nanotechnology. Trends Biotechnol. 35, 937−953. (30) Lavan, D. A., McGuire, T., and Langer, R. (2003) Small-scale systems for in vivo drug delivery. Nat. Biotechnol. 21, 1184. (31) Mahmoudi, M., Sant, S., Wang, B., Laurent, S., and Sen, T. (2011) Superparamagnetic iron oxide nanoparticles (SPIONs): development, surface modification and applications in chemotherapy. Adv. Drug Delivery Rev. 63, 24−46. (32) Bonoiu, A. C., Mahajan, S. D., Ding, H., Roy, I., Yong, K.-T., Kumar, R., Hu, R., Bergey, E. J., Schwartz, S. A., and Prasad, P. N. (2009) Nanotechnology approach for drug addiction therapy: gene silencing using delivery of gold nanorod-siRNA nanoplex in dopaminergic neurons. Proc. Natl. Acad. Sci. U. S. A. 106, 5546−5550. (33) Green-Sadan, T., Kuttner, Y., Lublin-Tennenbaum, T., Kinor, N., Boguslavsky, Y., Margel, S., and Yadid, G. (2005) Glial cell linederived neurotrophic factor-conjugated nanoparticles suppress acquisition of cocaine self-administration in rats. Exp. Neurol. 194, 97−105. (34) Rao, P., Yallapu, M. M., Sari, Y., Fisher, P. B., and Kumar, S. (2015) Designing novel nanoformulations targeting glutamate transporter excitatory amino acid transporter 2: Implications in treating drug addiction. Journal of personalized nanomedicine 1, 3. (35) Ignatowski, T., Aalinkeel, R., Reynolds, J., Nair, B., Sykes, D., Gleason, C., Law, W., Mammen, M., Prasad, P., Schwartz, S., and Mahajan, S. D. (2015) Nanotherapeutic Approach for Opiate Addiction Using DARPP-32 Gene Silencing in an Animal Model of Opiate Addiction. Journal of Neuroimmune Pharmacology 10, 136−152. (36) Chen, W., Peng, C., Jin, Z., Qiao, R., Wang, W., Zhu, S., Wang, L., Jin, Q., and Xu, C. (2009) Ultrasensitive immunoassay of 7aminoclonazepam in human urine based on CdTe nanoparticle bioconjugations by fabricated microfluidic chip. Biosens. Bioelectron. 24, 2051−2056. (37) Han, Z., Liu, H., Wang, B., Weng, S., Yang, L., and Liu, J. (2015) Three-dimensional surface-enhanced Raman scattering hotspots in spherical colloidal superstructure for identification and detection of drugs in human urine. Anal. Chem. 87, 4821−4828. (38) Dong, R., Weng, S., Yang, L., and Liu, J. (2015) Detection and direct readout of drugs in human urine using dynamic surfaceenhanced Raman spectroscopy and support vector machines. Anal. Chem. 87, 2937−2944. (39) Inscore, F., Shende, C., Sengupta, A., Huang, H., and Farquharson, S. (2011) Detection of drugs of abuse in saliva by surface-enhanced Raman spectroscopy (SERS). Appl. Spectrosc. 65, 1004−1008. (40) Farquharson, S., Shende, C., Sengupta, A., Huang, H., and Inscore, F. (2011) Rapid detection and identification of overdose drugs in saliva by surface-enhanced Raman scattering using fused gold colloids. Pharmaceutics 3, 425−439.

(41) Kaminsky, Y. G., Kosenko, E., Alexandrovich, Y. G., and Ataullakhanov, F. (2012) Experiments on alcocytes containing enzyme nanoparticles for reducing toxic blood concentration of ethanol. Bull. Exp. Biol. Med. 153, 170−172. (42) Ribeiro, J. A., Fernandes, P. M., Pereira, C. M., and Silva, F. (2016) Electrochemical sensors and biosensors for determination of catecholamine neurotransmitters: A review. Talanta 160, 653−679. (43) Krol, S., Macrez, R., Docagne, F., Defer, G., Laurent, S., Rahman, M., Hajipour, M. J., Kehoe, P. G., and Mahmoudi, M. (2013) Therapeutic benefits from nanoparticles: the potential significance of nanoscience in diseases with compromise to the blood brain barrier. Chem. Rev. 113, 1877−1903. (44) Gould, T. D., and Manji, H. K. (2005) DARPP-32: A molecular switch at the nexus of reward pathway plasticity. Proc. Natl. Acad. Sci. U. S. A. 102, 253−254. (45) Calabresi, P., Gubellini, P., Centonze, D., Picconi, B., Bernardi, G., Chergui, K., Svenningsson, P., Fienberg, A. A., and Greengard, P. (2000) Dopamine and cAMP-regulated phosphoprotein 32 kDa controls both striatal long-term depression and long-term potentiation, opposing forms of synaptic plasticity. J. Neurosci. 20, 8443−8451. (46) Valjent, E., Hervé, D., and Girault, J.-A. (2005) Drugs of abuse, protein phosphatases, and ERK pathway. Med Sci 21, 453. (47) Law, W.-C., Mahajan, S. D., Kopwitthaya, A., Reynolds, J. L., Liu, M., Liu, X., Chen, G., Erogbogbo, F., Vathy, L., and Aalinkeel, R. (2012) Gene silencing of human neuronal cells for drug addiction therapy using anisotropic nanocrystals. Theranostics 2, 695. (48) Umezaki, Y., Iohara, D., Anraku, M., Ishitsuka, Y., Irie, T., Uekama, K., and Hirayama, F. (2015) Preparation of hydrophilic C60 (OH) 10/2-hydroxypropyl-β-cyclodextrin nanoparticles for the treatment of a liver injury induced by an overdose of acetaminophen. Biomaterials 45, 115−123. (49) Daglish, M. R., Weinstein, A., Malizia, A. L., Wilson, S., Melichar, J. K., Britten, S., Brewer, C., Lingford-Hughes, A., Myles, J. S., Grasby, P., and Nutt, D. J. (2001) Changes in regional cerebral blood flow elicited by craving memories in abstinent opiate-dependent subjects. Am. J. Psychiatry 158, 1680−1686. (50) Lapchak, P., Araujo, D., and Collier, B. (1989) Regulation of endogenous acetylcholine release from mammalian brain slices by opiate receptors: hippocampus, striatum and cerebral cortex of guineapig and rat. Neuroscience 31, 313−325. (51) Ellgren, M., Spano, S. M., and Hurd, Y. L. (2007) Adolescent cannabis exposure alters opiate intake and opioid limbic neuronal populations in adult rats. Neuropsychopharmacology 32, 607. (52) Le Merrer, J., Becker, J. A., Befort, K., and Kieffer, B. L. (2009) Reward processing by the opioid system in the brain. Physiol. Rev. 89, 1379−1412. (53) Koob, G. F. (2011) Neurobiology of addiction. Focus 9, 55−65. (54) Mahmoudi, M., Hofmann, H., Rothen-Rutishauser, B., and Petri-Fink, A. (2012) Assessing the in vitro and in vivo toxicity of superparamagnetic iron oxide nanoparticles. Chem. Rev. 112, 2323− 2338. (55) Mahmoudi, M., Hosseinkhani, H., Hosseinkhani, M., Boutry, S., Simchi, A., Journeay, W. S., Subramani, K., and Laurent, S. (2011) Magnetic resonance imaging tracking of stem cells in vivo using iron oxide nanoparticles as a tool for the advancement of clinical regenerative medicine. Chem. Rev. 111, 253−280. (56) Sharifi, S., Seyednejad, H., Laurent, S., Atyabi, F., Saei, A. A., and Mahmoudi, M. (2015) Superparamagnetic iron oxide nanoparticles for in vivo molecular and cellular imaging. Contrast Media Mol. Imaging 10, 329−355. (57) Sagar, V., Atluri, V. S. R., Pilakka-Kanthikeel, S., and Nair, M. (2016) Magnetic nanotherapeutics for dysregulated synaptic plasticity during neuroAIDS and drug abuse. Mol. Brain 9, 57. (58) Sagar, V., Pilakka-Kanthikeel, S., Atluri, V. S., Ding, H., Arias, A. Y., Jayant, R. D., Kaushik, A., and Nair, M. (2015) Therapeutical neurotargeting via magnetic nanocarrier: implications to opiateinduced neuropathogenesis and neuroAIDS. J. Biomed. Nanotechnol. 11, 1722−1733. H


Perspective

ACS Chemical Neuroscience (59) He, M., Li, Z., Ge, Y., and Liu, Z. (2016) Portable upconversion nanoparticles-based paper device for field testing of drug abuse. Anal. Chem. 88, 1530−1534. (60) Krishnan, S., Alden, N., and Lee, K. (2015) Pathways and functions of gut microbiota metabolism impacting host physiology. Curr. Opin. Biotechnol. 36, 137−145. (61) Fukuda, S., and Ohno, H. (2014) Gut microbiome and metabolic diseases. Semin. Immunopathol. 36, 103−114. (62) Kinross, J. M., Darzi, A. W., and Nicholson, J. K. (2011) Gut microbiome-host interactions in health and disease. Genome Med. 3, 14. (63) Gérard, P. (2014) Metabolism of cholesterol and bile acids by the gut microbiota. Pathogens 3, 14−24. (64) David, L. A., Materna, A. C., Friedman, J., Campos-Baptista, M. I., Blackburn, M. C., Perrotta, A., Erdman, S. E., and Alm, E. J. (2014) Host lifestyle affects human microbiota on daily timescales. Genome Biology 15, R89. (65) Temko, J. E., Bouhlal, S., Farokhnia, M., Lee, M. R., Cryan, J. F., and Leggio, L. (2017) The Microbiota, the Gut and the Brain in Eating and Alcohol Use Disorders: A ‘Ménage à Trois’? Alcohol Alcohol. 52, 403−413. (66) Jangi, S., Gandhi, R., Cox, L. M., Li, N., Von Glehn, F., Yan, R., Patel, B., Mazzola, M. A., Liu, S., and Glanz, B. L. (2016) Alterations of the human gut microbiome in multiple sclerosis. Nat. Commun. 7, 12015. (67) Scheperjans, F., Aho, V., Pereira, P. A., Koskinen, K., Paulin, L., Pekkonen, E., Haapaniemi, E., Kaakkola, S., Eerola-Rautio, J., and Pohja, M. (2015) Gut microbiota are related to Parkinson’s disease and clinical phenotype. Mov. Disord. 30, 350−358. (68) Sampson, T. R., Debelius, J. W., Thron, T., Janssen, S., Shastri, G. G., Ilhan, Z. E., Challis, C., Schretter, C. E., Rocha, S., and Gradinaru, V. (2016) Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson’s disease. Cell 167, 1469− 1480 e1412. (69) Jose, P. A., and Raj, D. (2015) Gut microbiota in hypertension. Curr. Opin. Nephrol. Hypertens. 24, 403−409. (70) Forsythe, P., Bienenstock, J., and Kunze, W. A. (2014) Vagal Pathways for Microbiome-Brain-Gut Axis Communication. In Microbial Endocrinology: The Microbiota-Gut-Brain Axis in Health and Disease (Lyte, M., and Cryan, J. F., Eds.), pp 115−133, Springer. (71) O’Mahony, S. M., Clarke, G., Borre, Y. E., Dinan, T. G., and Cryan, J. F. (2015) Serotonin, tryptophan metabolism and the braingut-microbiome axis. Behav. Brain Res. 277, 32−48. (72) Clarke, G., Grenham, S., Scully, P., Fitzgerald, P., Moloney, R. D., Shanahan, F., Dinan, T. G., and Cryan, J. F. (2013) The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic system in a sex-dependent manner. Mol. Psychiatry 18, 666−673. (73) Akbarali, H. I., and Dewey, W. L. (2017) The gut-brain interaction in opioid tolerance. Curr. Opin. Pharmacol. 37, 126−130. (74) Nestler, E. J. (2004) Historical review: molecular and cellular mechanisms of opiate and cocaine addiction. Trends Pharmacol. Sci. 25, 210−218. (75) Valjent, E., Pascoli, V., Svenningsson, P., Paul, S., Enslen, H., Corvol, J.-C., Stipanovich, A., Caboche, J., Lombroso, P. J., and Nairn, A. C. (2005) Regulation of a protein phosphatase cascade allows convergent dopamine and glutamate signals to activate ERK in the striatum. Proc. Natl. Acad. Sci. U. S. A. 102, 491−496. (76) Nestler, E. J. (2001) Molecular basis of long-term plasticity underlying addiction. Nat. Rev. Neurosci. 2, 119. (77) Leclercq, S., De Saeger, C., Delzenne, N., de Timary, P., and Stärkel, P. (2014) Role of inflammatory pathways, blood mononuclear cells, and gut-derived bacterial products in alcohol dependence. Biol. Psychiatry 76, 725−733. (78) Taylor, A. (2018) Revealing a Brain-Gut Microbiome Connection Following Chronic Opioid Treatment. J. Pain 19, S1. (79) Eisenstein, T. K. (2011) Opioids and the immune system: what is their mechanism of action? British journal of pharmacology 164, 1826−1828.

(80) Schulzke, J. D., Ploeger, S., Amasheh, M., Fromm, A., Zeissig, S., Troeger, H., Richter, J., Bojarski, C., Schumann, M., and Fromm, M. (2009) Epithelial tight junctions in intestinal inflammation. Ann. N. Y. Acad. Sci. 1165, 294−300. (81) Hilburger, M. E., Adler, M. W., Truant, A. L., Meissler, J. J., Jr., Satishchandran, V., Rogers, T. J., and Eisenstein, T. K. (1997) Morphine induces sepsis in mice. J. Infect. Dis. 176, 183−188. (82) MacFarlane, A. S., Peng, X., Meissler, J. J., Jr., Rogers, T. J., Geller, E. B., Adler, M. W., and Eisenstein, T. K. (2000) Morphine increases susceptibility to oral Salmonella typhimurium infection. J. Infect. Dis. 181, 1350−1358. (83) Babrowski, T., Holbrook, C., Moss, J., Gottlieb, L., Valuckaite, V., Zaborin, A., Poroyko, V., Liu, D. C., Zaborina, O., and Alverdy, J. C. (2012) Pseudomonas aeruginosa virulence expression is directly activated by morphine and is capable of causing lethal gut derived sepsis in mice during chronic morphine administration. Ann. Surg. 255, 386. (84) Harari, Y., Weisbrodt, N. W., and Moody, F. G. (2006) The effect of morphine on mast cell−mediated mucosal permeability. Surgery 139, 54−60. (85) Brosnahan, A. J., Jones, B. J., Dvorak, C. M., and Brown, D. R. (2014) Morphine attenuates apically-directed cytokine secretion from intestinal epithelial cells in response to enteric pathogens. Pathogens 3, 249−257. (86) Wang, F., and Roy, S. (2017) Gut Homeostasis, Microbial Dysbiosis, and Opioids. Toxicol. Pathol. 45, 150−156. (87) Keshishian, H., Burgess, M. W., Specht, H., Wallace, L., Clauser, K. R., Gillette, M. A., and Carr, S. A. (2017) Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nat. Protoc. 12, 1683. (88) Monopoli, M. P., Åberg, C., Salvati, A., and Dawson, K. A. (2012) Biomolecular coronas provide the biological identity of nanosized materials. Nat. Nanotechnol. 7, 779. (89) Nel, A. E., Mädler, L., Velegol, D., Xia, T., Hoek, E. M., Somasundaran, P., Klaessig, F., Castranova, V., and Thompson, M. (2009) Understanding biophysicochemical interactions at the nano− bio interface. Nat. Mater. 8, 543. (90) Mahmoudi, M., Lynch, I., Ejtehadi, M. R., Monopoli, M. P., Bombelli, F. B., and Laurent, S. (2011) Protein− nanoparticle interactions: opportunities and challenges. Chem. Rev. 111, 5610− 5637. (91) Vilaseca, P., Dawson, K. A., and Franzese, G. (2013) Understanding and modulating the competitive surface-adsorption of proteins through coarse-grained molecular dynamics simulations. Soft Matter 9, 6978. (92) Kharazian, B., Hadipour, N. L., and Ejtehadi, M. R. (2016) Understanding the nanoparticle-protein corona complexes using computational and experimental methods. Int. J. Biochem. Cell Biol. 75, 162−174. (93) Distler, U., and Tenzer, S. (2017) Tools for Pathogen Proteomics: Fishing with Biomimetic Nanosponges. ACS Nano 11, 11768−11772. (94) Wang, W., Sedykh, A., Sun, H., Zhao, L., Russo, D. P., Zhou, H., Yan, B., and Zhu, H. (2017) Predicting Nano−Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. ACS Nano 11, 12641−12649. (95) Lundqvist, M., Stigler, J., Elia, G., Lynch, I., Cedervall, T., and Dawson, K. A. (2008) Nanoparticle size and surface properties determine the protein corona with possible implications for biological impacts. Proc. Natl. Acad. Sci. U. S. A. 105, 14265−14270. (96) Mahmoudi, M., Abdelmonem, A. M., Behzadi, S., Clement, J. H., Dutz, S., Ejtehadi, M. R., Hartmann, R., Kantner, K., Linne, U., and Maffre, P. (2013) Temperature: the “ignored” factor at the nanobio interface. ACS Nano 7, 6555−6562. (97) Mueller, L. K., Simon, J., Rosenauer, C., Mailänder, V., Morsbach, S., and Landfester, K. (2018) The transferability from animal models to humans: challenges regarding aggregation and protein corona formation of nanoparticles. Biomacromolecules 19, 374. I


Perspective

ACS Chemical Neuroscience (98) Serpooshan, V., Sheibani, S., Pushparaj, P., Wojcik, M., Jang, A. Y., Santoso, M. R., Jang, J. H., Huang, H., Safavi-Sohi, R., Haghjoo, N., Nejadnik, H., Aghaverdi, H., Vali, H., Kinsella, J. M., Presley, J., Xu, K., Yang, P. C.-M., and Mahmoudi, M. (2018) Effect of Cell Sex on Uptake of Nanoparticles: The Overlooked Factor at the Nanobio Interface. ACS Nano 12, 2253−2266. (99) Mahmoudi, M. (2018) Debugging Nano−Bio Interfaces: Systematic Strategies to Accelerate Clinical Translation of Nanotechnologies. Trends Biotechnol., 1 DOI: 10.1016/j.tibtech.2018.02.014. (100) Kennedy, L., Mehl, T. D., Elder, E., Varghese, M., and Merimee, T. J. (1982) Nonenzymatic glycosylation of serum and plasma proteins. Diabetes 31, 52−56. (101) Engström, G., Lind, P., Hedblad, B., Stavenow, L., Janzon, L., and Lindgärde, F. (2002) Effects of cholesterol and inflammationsensitive plasma proteins on incidence of myocardial infarction and stroke in men. Circulation 105, 2632−2637. (102) Lin, Y.-P., Yang, C.-Y., Liao, C.-C., Yu, W.-C., Chi, C.-W., and Lin, C.-H. (2012) Plasma protein characteristics of long-term hemodialysis survivors. PLoS One 7, e40232. (103) Acharya, S., and Dimichele, D. (2008) Rare inherited disorders of fibrinogen. Haemophilia 14, 1151−1158. (104) Kelly-Spratt, K. S., Pitteri, S. J., Gurley, K. E., Liggitt, D., Chin, A., Kennedy, J., Wong, C.-H., Zhang, Q., Buson, T. B., and Wang, H. (2011) Plasma proteome profiles associated with inflammation, angiogenesis, and cancer. PLoS One 6, e19721. (105) Bowen, D. J. (2002) Haemophilia A and haemophilia B: molecular insights. Mol. Pathol. 55, 1. (106) Blennow, K., Hampel, H., Weiner, M., and Zetterberg, H. (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat. Rev. Neurol. 6, 131−144. (107) Ridker, P. M., Brown, N. J., Vaughan, D. E., Harrison, D. G., and Mehta, J. L. (2004) Established and emerging plasma biomarkers in the prediction of first atherothrombotic events. Circulation 109, IV6−IV-19. (108) El-Agnaf, O. M., Salem, S. A., Paleologou, K. E., Curran, M. D., Gibson, M. J., Schlossmacher, M. G., and Allsop, D. (2006) Detection of oligomeric forms of α-synuclein protein in human plasma as a potential biomarker for Parkinson’s disease. FASEB J. 20, 419−425. (109) Hanash, S. M., Pitteri, S. J., and Faca, V. M. (2008) Mining the plasma proteome for cancer biomarkers. Nature 452, 571−579. (110) Wei, J., Gao, W., Zhu, C.-J., Liu, Y.-Q., Mei, Z., Cheng, T., and Shu, Y.-Q. (2011) Identification of plasma microRNA-21 as a biomarker for early detection and chemosensitivity of non−small cell lung cancer. Aizheng 30, 407. (111) Hajipour, M. J., Laurent, S., Aghaie, A., Rezaee, F., and Mahmoudi, M. (2014) Personalized protein coronas: a “key” factor at the nanobiointerface. Biomater. Sci. 2, 1210−1221. (112) Caracciolo, G., Farokhzad, O. C., and Mahmoudi, M. (2017) Biological Identity of Nanoparticles In Vivo: Clinical Implications of the Protein Corona. Trends Biotechnol. 35, 257−264. (113) Hajipour, M. J., Raheb, J., Akhavan, O., Arjmand, S., Mashinchian, O., Rahman, M., Abdolahad, M., Serpooshan, V., Laurent, S., and Mahmoudi, M. (2015) Personalized disease-specific protein corona influences the therapeutic impact of graphene oxide. Nanoscale 7, 8978−8994. (114) Corbo, C., Molinaro, R., Tabatabaei, M., Farokhzad, O. C., and Mahmoudi, M. (2017) Personalized protein corona on nanoparticles and its clinical implications. Biomater. Sci. 5, 378−387. (115) Caputo, D., Papi, M., Coppola, R., Palchetti, S., Digiacomo, L., Caracciolo, G., and Pozzi, D. (2017) A protein corona-enabled blood test for early cancer detection. Nanoscale 9, 349−354. (116) Strojan, K., Leonardi, A., Bregar, V. B., Križaj, I., Svete, J., and Pavlin, M. (2017) Dispersion of nanoparticles in different media importantly determines the composition of their protein corona. PLoS One 12, e0169552. (117) Vlasova, M., and Smirin, B. V. (2017) Personalized Approach in Nanomedicine: Understanding Adverse Effects and Their Risk Assessment. Recent Advances in Drug Delivery Technology, 1−21.

(118) Park, S.-M., Aalipour, A., Vermesh, O., Yu, J. H., and Gambhir, S. S. (2017) Towards clinically translatable in vivo nanodiagnostics. Nature Reviews Materials 2, 17014. (119) Colapicchioni, V., Tilio, M., Digiacomo, L., Gambini, V., Palchetti, S., Marchini, C., Pozzi, D., Occhipinti, S., Amici, A., and Caracciolo, G. (2016) Personalized liposome−protein corona in the blood of breast, gastric and pancreatic cancer patients. Int. J. Biochem. Cell Biol. 75, 180−187. (120) Choi, H., Lee, Y.-S., Hwang, D. W., and Lee, D. S. (2016) Translational radionanomedicine: a clinical perspective. Eur. J. Nanomed. 8, 71−84. (121) Tavakol, M., Montazeri, A., Naghdabadi, R., Hajipour, M., Zanganeh, S., Caracciolo, G., and Mahmoudi, M. (2018) Diseaserelated metabolites affect protein-nanoparticle interactions. Nanoscale 10, 7108. (122) Salomon, F., Cuneo, R. C., Hesp, R., and Sönksen, P. H. (1989) The effects of treatment with recombinant human growth hormone on body composition and metabolism in adults with growth hormone deficiency. N. Engl. J. Med. 321, 1797−1803. (123) Beger, R. D., Sun, J., and Schnackenberg, L. K. (2010) Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity. Toxicol. Appl. Pharmacol. 243, 154− 166. (124) Goldstein, A. (1949) The interactions of drugs and plasma proteins. Pharmacol. Rev. 1, 102−165. (125) Martin, B. (1965) Potential effect of the plasma proteins on drug distribution. Nature 207, 274. (126) Davis, B. D. (1943) The binding of sulfonamide drugs by plasma proteins. A factor in determining the distribution of drugs in the body. J. Clin. Invest. 22, 753−762. (127) Leggett, R., Lee-Smith, E. E., Jickells, S. M., and Russell, D. A. (2007) Intelligent” fingerprinting: simultaneous identification of drug metabolites and individuals by using antibody-functionalized nanoparticles. Angew. Chem. 119, 4178−4181. (128) Hazarika, P., Jickells, S. M., Wolff, K., and Russell, D. A. (2008) Imaging of latent fingerprints through the detection of drugs and metabolites. Angew. Chem., Int. Ed. 47, 10167−10170. (129) Matsumoto, M., Kibe, R., Ooga, T., Aiba, Y., Kurihara, S., Sawaki, E., Koga, Y., and Benno, Y. (2012) Impact of intestinal microbiota on intestinal luminal metabolome. Sci. Rep. 2, 233. (130) Wikoff, W. R., Anfora, A. T., Liu, J., Schultz, P. G., Lesley, S. A., Peters, E. C., and Siuzdak, G. (2009) Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl. Acad. Sci. U. S. A. 106, 3698−3703. (131) Clarke, G., Stilling, R. M., Kennedy, P. J., Stanton, C., Cryan, J. F., and Dinan, T. G. (2014) Minireview: Gut microbiota: the neglected endocrine organ. Mol. Endocrinol. 28, 1221−1238. (132) Kennedy, P. J., Cryan, J. F., Dinan, T. G., and Clarke, G. (2017) Kynurenine pathway metabolism and the microbiota-gut-brain axis. Neuropharmacology 112, 399−412. (133) de la Fuente-Nunez, C., Meneguetti, B. T., Franco, O. L., and Lu, T. K. (2018) Neuromicrobiology: How Microbes Influence the Brain. ACS Chem. Neurosci. 9, 141−150. (134) Saha, K., Agasti, S. S., Kim, C., Li, X., and Rotello, V. M. (2012) Gold nanoparticles in chemical and biological sensing. Chem. Rev. 112, 2739−2779. (135) Zheng, T., Pierre-Pierre, N., Yan, X., Huo, Q., Almodovar, A. J. O., Valerio, F., Rivera-Ramirez, I., Griffith, E., Decker, D. D., Chen, S., and Zhu, N. (2015) Gold Nanoparticle-Enabled Blood Test for Early Stage Cancer Detection and Risk Assessment. ACS Appl. Mater. Interfaces 7, 6819−6827. (136) Zheng, T., Finn, C., Parrett, C. J., Dhume, K., Hwang, J. H., Sidhom, D., Strutt, T. M., Li Sip, Y. Y., McKinstry, K. K., and Huo, Q. (2017) A Rapid Blood Test To Determine the Active Status and Duration of Acute Viral Infection. ACS Infect. Dis. 3, 866−873. (137) Du, Y., Li, B., Guo, S., Zhou, Z., Zhou, M., Wang, E., and Dong, S. (2011) G-Quadruplex-based DNAzyme for colorimetric detection of cocaine: Using magnetic nanoparticles as the separation and amplification element. Analyst 136, 493−497. J


Perspective

ACS Chemical Neuroscience (138) Goldberg, D. E., and Holland, J. H. (1988) Genetic algorithms and machine learning. Machine learning 3, 95−99. (139) Deo, R. C. (2015) Machine learning in medicine. Circulation 132, 1920−1930. (140) Murdoch, T. B., and Detsky, A. S. (2013) The inevitable application of big data to health care. Jama 309, 1351−1352. (141) McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., and Barton, D. (2012) Big data: the management revolution. Harvard business review 90, 60−68. (142) Cohen, J. D., Li, L., Wang, Y., Thoburn, C., Afsari, B., Danilova, L., Douville, C., Javed, A. A., Wong, F., Mattox, A., Hruban, R. H., Wolfgang, C. L., Goggins, M. G., Dal Molin, M., Wang, T.-L., Roden, R., Klein, A. P., Ptak, J., Dobbyn, L., Schaefer, J., Silliman, N., Popoli, M., Vogelstein, J. T., Browne, J. D., Schoen, R. E., Brand, R. E., Tie, J., Gibbs, P., Wong, H.-L., Mansfield, A. S., Jen, J., Hanash, S. M., Falconi, M., Allen, P. J., Zhou, S., Bettegowda, C., Diaz, L., Tomasetti, C., Kinzler, K. W., Vogelstein, B., Lennon, A. M., and Papadopoulos, N. (2018) Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926−930. (143) Yan, J., and Charles, J. F. (2018) Gut microbiota and IGF-1. Calcif. Tissue Int. 102, 406−414. (144) Hsiao, E. Y., McBride, S. W., Hsien, S., Sharon, G., Hyde, E. R., McCue, T., Codelli, J. A., Chow, J., Reisman, S. E., and Petrosino, J. F. (2013) Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155, 1451− 1463. (145) Tillisch, K., Labus, J., Kilpatrick, L., Jiang, Z., Stains, J., Ebrat, B., Guyonnet, D., Legrain-Raspaud, S., Trotin, B., Naliboff, B., and Mayer, E. A. (2013) Consumption of fermented milk product with probiotic modulates brain activity. Gastroenterology 144, 1394−1401. (146) Bravo, J. A., Forsythe, P., Chew, M. V., Escaravage, E., Savignac, H. M., Dinan, T. G., Bienenstock, J., and Cryan, J. F. (2011) Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc. Natl. Acad. Sci. U. S. A. 108, 16050−16055. (147) Acosta-Martinez, V., Dowd, S., Sun, Y., and Allen, V. (2008) Tag-encoded pyrosequencing analysis of bacterial diversity in a single soil type as affected by management and land use. Soil Biol. Biochem. 40, 2762−2770. (148) Partty, A., Kalliomaki, M., Wacklin, P., Salminen, S., and Isolauri, E. (2015) A possible link between early probiotic intervention and the risk of neuropsychiatric disorders later in childhood: a randomized trial. Pediatr. Res. 77, 823−828. (149) Desbonnet, L., Garrett, L., Clarke, G., Bienenstock, J., and Dinan, T. G. (2008) The probiotic Bifidobacteria infantis: An assessment of potential antidepressant properties in the rat. J. Psychiatr. Res. 43, 164−174. (150) Fröhlich, E. E., and Fröhlich, E. (2016) Cytotoxicity of nanoparticles contained in food on intestinal cells and the gut microbiota. Int. J. Mol. Sci. 17, 509. (151) Van Den Brûle, S., Ambroise, J., Lecloux, H., Levard, C., Soulas, R., De Temmerman, P.-J., Palmai-Pallag, M., Marbaix, E., and Lison, D. (2015) Dietary silver nanoparticles can disturb the gut microbiota in mice. Part. Fibre Toxicol. 13, 38. (152) Javurek, A. B., Suresh, D., Spollen, W. G., Hart, M. L., Hansen, S. A., Ellersieck, M. R., Bivens, N. J., Givan, S. A., Upendran, A., Kannan, R., and Rosenfeld, C. S. (2017) Gut dysbiosis and neurobehavioral alterations in rats exposed to silver nanoparticles. Sci. Rep. 7, 2822. (153) Williams, K., Milner, J., Boudreau, M. D., Gokulan, K., Cerniglia, C. E., and Khare, S. (2015) Effects of subchronic exposure of silver nanoparticles on intestinal microbiota and gut-associated immune responses in the ileum of Sprague-Dawley rats. Nanotoxicology 9, 279−289. (154) Fallon, M. S., Varshney, M., Dennis, D. M., and Chauhan, A. (2004) A physiologically-based pharmacokinetic model of drug detoxification by nanoparticles. J. Pharmacokinet. Pharmacodyn. 31, 381−400.

(155) Wolfbeis, O. S. (2009) Nanoparticle-Enhanced Fluorescence Imaging of Latent Fingerprints Reveals Drug Abuse. Angew. Chem., Int. Ed. 48, 2268−2269.

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