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Computational approaches in design of nucleic acid-based therapeutics
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ScienceDirect Computational approaches in design of nucleic acid-based therapeutics Mark Sherman1 and Lydia Contreras2 Recent advances in computational and experimental methods have led to novel avenues for therapeutic development. Utilization of nucleic acids as therapeutic agents and/or targets has been recently gaining attention due to their potential as high-affinity, selective molecular building blocks for various therapies. Notably, development of computational algorithms for predicting accessible RNA binding sites, identifying therapeutic target sequences, modeling delivery into tissues, and designing binding aptamers have enhanced therapeutic potential for this new drug category. Here, we review trends in drug development within the pharmaceutical industry and ways by which nucleic acid-based drugs have arisen as effective therapeutic candidates. In particular, we focus on computational and experimental approaches to nucleic acidbased drug design, commenting on challenges and outlooks for future applications. Addresses 1 Cell and Molecular Biology Graduate Program, University of Texas at Austin, 100 E. 24th Street, A6500, Austin, TX 78712, USA 2 McKetta Department of Chemical Engineering, University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA Corresponding author: Contreras, Lydia ([email protected])
Current Opinion in Biotechnology 2018, 53:232–239 This review comes from a themed issue on Pharmaceutical biotechnology Edited by Amanda Lewis and Nripen Singh
https://doi.org/10.1016/j.copbio.2017.12.001 0958-1669/ã 2018 Published by Elsevier Ltd.
Introduction Over almost 80 years of FDA regulation, the field of drug discovery and development has transformed its focus from pain management of disease symptoms to systematic identification of causative factors and targeted mediation (Figure 1). Early pharmaceutical development stemmed from the isolation and concentration of active agents from natural products by chance discovery. Although the therapeutic mechanisms of these substances were unclear at the time, most contemporary drug development efforts aim to target specific disease-associated proteins. The majority of approaches involve screening small molecule Current Opinion in Biotechnology 2018, 53:232–239
libraries for target protein inhibition that lead to therapeutic effects. While these efforts have resulted in hundreds of new drug entities, the journey to approval for the average drug is increasingly costly [1]. The rising cost of drug development can, in part, be attributed to increased screening efforts due to low hit rates and to misdirected investigations based on proteins that show no interactions with current candidate drug molecule libraries; other reasons include the high probability of failure during the scale-up process and the escalating demand by regulatory bodies for more safety and efficacy data prior to approval [1,2]. Furthermore, many diseases simply cannot be influenced by small molecules due to the inability to identify a binding pocket in key proteins underlying disease pathways to serve as a suitable drug target. In addition, challenges to small molecules arise from the possibility that disease phenotypes are caused by mechanisms upstream of protein production, such as improper gene splicing, without a suitable small molecule target. In recent years, drug development efforts have become more sophisticated, relying less on large-scale screening of chemical compounds and more on targeted development of drugs toward specific molecular entities. Modern drug development pipelines attempt to target a wider spectrum of molecules beyond traditional enzyme targets, including metabolites [3], protein–protein interactions [4], and nucleic acids [5]. These advances have enabled treatment of various diseases through the development and engineering of new vaccines (with over 30 active vaccine-associated clinical trials currently underway (clinicaltrials.gov)), peptide therapeutics such as Parsabiv [6], nucleotide prodrugs such as Vosevi [7], as well as a variety of antibody-based technologies including antibody–drug conjugates (used in Mylotarg) [8,9], phage display (used in development of Humira) [10,11,12], and radiolabelled diagnostic antibodies as used in Netspot [13–15].
Incentives and challenges to nucleic-acid therapeutic development While protein-based therapeutics (antibodies, recombinant proteins, and peptides) have made up over a quarter of FDA approved drugs since 2015 (FDA.gov), they have relatively a short shelf life, require refrigerated transport, and rely on live organisms for production, making manufacturing them expensive, contamination prone, and variable across batches [16]. In response to these shortcomings, recent efforts have been made toward development of nucleic acid-based drugs, offering the key advantage of chemical manufacturing. As such, they www.sciencedirect.com
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Figure 1
1939-1959 N= 27
1960-1999 N=111
1990-2017 N=357 Current Opinion in Biotechnology
An analysis of indications and usage descriptions from FDA approved drug labels across three time periods from 1939 to 2017. Between 1939 and 1959, approved drugs appear to be associated with pain management and symptomatic relief. Between 1960 and 1989, small molecule inhibitors appear to become mainstream and birth control is developed. Between 1990 and 2017, inhibition continued to be a major theme for pharmaceutical development and drugs became more specific toward particular diseases, including viruses such as HIV. During the last 30 years, the emergence of nucleic acids as targets (nucleosides) also represents a newer phenomenon. Data generated by an analysis on a collection of drug labels through U.S. National Library of Medicine’s DailyMed website.
maintain a relatively long half life, are easily transportable, are consistent from batch to batch, and have the potential to introduce new therapeutic chemistries [17] to the current, inhibitor-dominated field (Figure 1). For instance, DNA has been identified as a target for drug development [18] for its gene therapy ability via manipulation of gene expression or editing of defective genes [19]. Likewise, RNA variants have been implicated in a variety of disease phenotypes such as Spinal Muscular Atrophy (SMA) [20], Duchenne Muscular Dystrophy (DMD) [21,22], Alzheimer’s Disease, and cancer [23,24]. Therefore, development of nucleic acid-based drugs hold promise to provide high affinity and high specificity entities for therapeutic applications; however, progress in this field has been limited by challenges in delivery to specific tissues, a very active area of research (discussed later) [25–31]. In 2016, three nucleic acid-based therapies gained FDA approval: Exondys-51 [22] (for treatment of DMD), Spinraza [20] (for treatment of SMA), and Defitelio [32] (for treatment of renal or pulmonary dysfunction). Both Spinraza and Exondys-51 are antisense oligonucleotides that therapeutically influence splicing patterns of diseased proteins. The mechanism for Defitelio has not been elucidated [32]. Several other RNA-based or RNA-targeting drugs are currently in clinical trials (Table 1). The majority of these candidate drugs involve www.sciencedirect.com
infusion of chimeric antigen receptor (CAR) T cells while others are siRNA (small interfering RNA), shRNA (small hairpin RNA), and mRNA (messenger RNA) based. CAR T cells are derived from the patients own T cells which are genetically modified ex vivo and introduced back into the body with enhanced therapeutic functions. Currently approved RNA-based drugs are administered as local injections or systemic infusions (Table 1). As research in nucleic acid-based therapeutics progresses, it holds promise to develop drugs that can not only inhibit action of a protein, but could alter gene expression and metabolic flux through development of aptamers (RNA analogs of antibodies), prodrugs, siRNAs, miRNAs, and new vaccines with more efficient modes of delivery.
Current strategies toward aptamer-based therapeutic development Due to their ability to bind to diverse targets such as ions, dyes, amino acids, RNAs, oligosaccharides, antibodies, and cells, aptamers have become the main focus of nucleic acid-based therapeutic development [33]. Aptamers are short, single-stranded DNA or RNA oligonucleotides that bind to a variety of target molecules with high affinity and specificity, dependent on their tertiary structure [34]. The majority of DNA and RNA aptamers are experimentally developed via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) Current Opinion in Biotechnology 2018, 53:232–239
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Table 1 Nucleic acid-based drugs recently approved or currently in clinical trials Current phase
Active ingredient/indicated study
Indicated condition
Mode of delivery
Intervention
Early Non-Viral, RNA-Redirected Phase 1 Autologous T Cells
Hodgkin Lymphoma
Intravenous (IV) infusion
Early Phase 1 Phase 1 Phase 1 Phase 1
Autologous T Cells Expressing MET scFv CAR (RNA CART-cMET) RNActive1 Rabies Vaccine (CV7201) Hypoxia-inducible Factor 1a (HIF1A) Pbi-shRNATM EWS/FLI1 Type 1 LPX
Malignant Melanoma Breast Cancer Rabies Carcinoma, Hepatocellular Ewing’s Sarcoma
Intravenous (IV) infusion
Phase 1
Neutral Liposomal Small Interfering RNA Delivery of EphA2 cMet CAR RNA T Cells
Advanced Cancers
Intravenous (IV) infusion
CD19 RNA redirected autologous T-cells (RNA CART19 cells) T cells modified with RNA anti-cMET CAR mRNA mRNA Antagonist pbi-shRNATM EWS/FLI1 Type 1 LPX siRNA-EphA2-DOPC
Metastatic Breast Cancer Triple Negative Breast Cancer Malignant Neoplasms Brain
Intratumor administration
cMet RNA CAR T cells
Intravenous (IV) infusion
RNA-loaded dendritic cell vaccine; Basiliximab
Sickle Cell Disease
Intravenous (IV) infusion
Phase 1
Phase 1
Intravenous (IV) injection Intravenous (IV) infusion Intravenous (IV) infusion
Phase 1
Basiliximab in Treating Patients With Newly Diagnosed Glioblastoma Multiforme Undergoing Targeted Immunotherapy and Temozolomide-Caused Lymphopenia Gene Transfer
Phase 1
CD19+ CAR T Cells
Leukemia Lymphoma
Intravenous (IV) infusion
Phase 3
Dendritic Cells loaded with Autologous Tumor RNA
Uveal Melanoma
Intravenous (IV) infusion
Approved
Nusinersen (Spinraza)
Intrathecal injection
Approved
Eteplirsen (Exondys 51)
Intravenous (IV) infusion
Antisense Oligonucleotide
Approved
Defibrotid (Defiteli)
Spinal Muscular Atrophy (SMA) Duchenne muscular dystrophy (DMD) Severe hepatic venoocclusive disease (sVOD)
CD34+ HSC cells transduced with lentiviral vector containing shRNA targeting BCL11a Fludarabine monophosphate; Cyclophosphamide Autologous Dendritic Cells loaded with autologous Tumor RNA Antisense Oligonucleotide
Intravenous (IV) infusion
Oligonucleotide
Data collected from FDA Novel Drug Approval summaries (FDA.gov) and ClinicalTrials.gov.
[35,36], a process that utilizes multiple rounds of ligand selection and amplification of RNA or DNA library variants to raise aptamers with high affinity and selectivity to a target ligand. Aptamers can bind in the pM range of affinity and exhibit higher specificity and lower off-target effects than their protein counterparts [37]. The utilization of SELEX has contributed greatly in our understanding of RNA biology and development of nucleic acidbased therapeutics; however, its successful application to aptamers with desirable properties requires 10–20 rounds of selection, representing weeks to months of development effort [16]. While the coupling of SELEX with other molecular technologies such as high throughput sequencing, microarrays and bioinformatics have shown a reduction in selection efforts [38–41], computational modeling approaches for the rational design of aptamers, particularly RNA aptamers, show promise to greatly reduce development time and associated costs [16]. Current Opinion in Biotechnology 2018, 53:232–239
Role of computational approaches in aptamer-based therapeutic design The structural makeup of aptamers are key for their use as effective therapeutics [33,34]; therefore, development of structure prediction algorithms represents a significant undertaking in the rational design of aptamer therapeutics. Structural predictions from nucleic acid sequences are challenging due to structural differences observed in vivo versus in vitro [42], a high number of transitional states [43], and the influence of metal ions, other RNAs, small organic molecules, and proteins on 3D RNA structure [33,34,44]. To address these challenges, bioinformatics and computational modeling approaches have been constructed to statistically analyze existing datasets to increase the likelihood of successful aptamer design. In particular, databases of RNA-RNA interactions [45], RNA-DNA interactions [46], and nucleic acid-protein interactions [47] have been compiled to assist in these efforts [16]. www.sciencedirect.com
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In this scheme, once a target for the aptamer has been identified, structural data of both the target and the ideal aptamer is gathered or generated. This is generally done through crystallography-based approaches [48], homology modeling using modeling software like ModeRNA [49– 51], or through the use of a thermodynamics-based model such as RNAup [52] (Table 2). Furthermore, several models have been introduced in recent years to derive secondary structure from primary RNA sequence such as mFold [53] and ViennaRNA [54] (Table 2). In general, these models take into account a series of user defined constraints to predict RNA secondary structure through energy minimization algorithms. Models have also been developed to predict 3D tertiary structure from secondary structure (MC-FOLDjMC-Sym [55]) and even directly from primary sequence using coarse grained molecular dynamics approaches (NAST [56], ERNWIN [57]) (Table 2). A graphical summary of these models based on complexity is shown in Figure 2. For modeling RNA– RNA interactions of uncharacterized RNAs, a desirable metric is the identification of accessible regions of target RNAs in vivo [50]. To address this challenge, models have been developed and benchmarked to investigate the likelihood for RNA–RNA binding. These include IntaRNA [58], an RNA–RNA prediction algorithm based on seed constrains, and InTherAcc [43], an informed biophysical model based on biological data collected in vivo capturing RNA accessibility for binding using an in vivo RNA Structural Sensing System [42]. To help inform crystal structure determination and provide experimental validation for models, several biochemical techniques have been useful; these include hydroxyl radical footprinting (probing for protected nucleotides), selective 20 OH acylation by primer extension (SHAPE) (chemical probing for backbone flexibility), and dimethysulfate sequencing (probing for H bonding) [59].
Once tertiary structure is established, models can be utilized to predict docking location and strength of binding of nucleic acids and their targets based on atomistic simulations. These simulations calculate minimum free energy based on parameters such as electrostatic interactions, Van der Waals forces, and hydrophobic interactions. Atomistic modeling tools such as CHARMM [60] and AMBER [61] have been used in this fashion to simulate RNA–metal interactions and several examples of this approach have recently been reviewed in Sun et al. [50]. For instance, molecular simulations and Markov state modeling have been applied to predict the kinetics of RNA conformational state changes, supporting experimental observations that the binding of specific RNAs can be relatively slow and involves large conformational rearrangements after initial binding [62]. Understanding these dynamics can allow for efficient use of informatics tools toward rational design of therapeutic aptamers, a process analogous to approaches used in protein engineering for therapeutics [63].
Rational design of nucleic acid therapeutic delivery systems While the molecular toolkit of nucleic acid-based drugs is being diversified and potential therapeutic mechanisms are being elucidated, the challenge to effectively deliver these therapeutic agents to the site of treatment remains a major hurdle. That is, for a therapeutic molecule to reach its target site, several aspects need to be addressed; a few key ones include prevention of immunogenicity, escape from degradation enzymes, penetration through cellular barriers that inhibit uptake, release from liposomes before degradation, and (depending on its mechanism) transport to the nucleus to perform intended functions [64,65]. While nucleic acid-based drugs have been experimentally optimized for individual elements [64], there is not yet an
Table 2 A collection of modeling programs referenced in this article, their URL address, and a short description of their function Model
Website
ModeRNA MC-FOLDjMC-Sym NAST
http://genesilico.pl/moderna/ http://www.major.iric.ca/MC-Fold/ https://simtk.org/projects/nast
ERNWIN IntaRNA
http://rna.tbi.univie.ac.at/ernwin http://rna.informatik.uni-freiburg.de/IntaRNA/ Input.jsp http://rna.tbi.univie.ac.at/cgi-bin/ RNAWebSuite/RNAup.cgi http://unafold.rna.albany.edu/?q=mfold https://www.tbi.univie.ac.at/RNA/
RNAup mFold ViennaRNA AutoDock Vina HADDOCK PatchDock
http://vina.scripps.edu/ http://www.bonvinlab.org/software/haddock2. 2/haddock-start/ https://bioinfo3d.cs.tau.ac.il/PatchDock/
Description Comparative modeling of RNA 3D structures Predicts secondary structures from sequence Uses coarse grained molecular dynamics and a knowledge-based force field to generate RNA structures Predicts RNA 3D structure using a coarse-grain helix-centered model Prediction of interactions between two RNA molecules Predicts RNA-RNA interactions Predicts nucleic acid folding and hybridization A C code library and stand-alone programs for prediction and comparison of RNA secondary structures Molecular docking program to calculate free energy scores High Ambiguity Driven Biomolecular Docking based on Biochemical and/or biophysical information Molecular Docking Algorithm Based on Shape Complementarity Principles
Data collected from model-associated publications and their web portals.
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Figure 2
Current Opinion in Biotechnology
Graphical depiction of nucleic acid-based models and their uses across three levels of complexity: 2D RNA structure prediction from primary sequence, prediction of RNA–RNA interactions, and prediction of RNA–protein interactions. Data collected from model-associated publications and their web portals.
effective vehicle for targeted drug delivery. Current therapeutic delivery relies on transport of naked particles or suboptimal modes of delivery, such as PEGylation and liposomal based delivery [27,66]. Multi-scale computational approaches to these issues could complement experimental methods. Indeed, these approaches have been implicated both as tools to gain mechanistic insights on biological properties and as inspirations to develop novel systems for nanoparticle delivery [27]. Specifically, the coarse grained molecular dynamics simulation model, MARTINI [67], has been used to model RNA interactions with other biomolecules and has been implicated for use in nanoparticle-mediated delivery modeling [27,68,69].
distant future, it is possible that genetically engineered replacement organs could be delivered overnight for transplantation the following day [71]. The societal outcome of these revolutions may transition the role of health care from one of fixing health issues to one of surveillance followed by immediate treatment in response to detection of early disease indicators. Nucleic acid-based technologies, such as viral gene therapy and genome editing, enable the potential for permanent removal of diseases without sustained medication nor drug development for treatment of disease phenotypes. This type of therapy could reduce health care costs and could curtail the impact of complex diseases worldwide.
Conclusions Future directions of nucleic acid-based therapeutics Developments in DNA and RNA sequencing technologies, genome editing capabilities, and bioinformatics approaches hold promise to replace dependence of a comfortable lifestyle on pharmaceuticals with advanced control of gene expression, particularly for complex diseases caused by genetic anomalies [70]. As computational technologies become inextricably incorporated into society, their role in biotechnology and therapeutics is likely to expand. In the near future, these new technologies raise the possibility that a drop of blood placed on a device similar to a glucose monitor could screen the genome, transcriptome, proteome and metabolome for abnormalities and those anomalies could be medicated appropriately before the onset of disease. In a perhaps not so Current Opinion in Biotechnology 2018, 53:232–239
Nucleic acid-based therapeutics are quickly becoming relevant within the pharmaceutical industry. With three oligonucleotide based drugs approved in 2016 and over 40 privately in development [72] (ClinicalTrials.gov), the nucleic acid-based therapeutic industry appears to be gathering momentum. Additionally, increasing attention by the academic research community toward new tools and delivery mechanisms for DNA and RNA oligonucleotides suggests that we are not far off from advances in delivery to specific cell types and tissues, providing a new suite of therapeutic chemistries with fewer side effects and more efficient modes of action, rivaling antibodybased therapeutics. The development of computational tools to identify target molecules (DNA, RNA, protein, small molecules) and modeling approaches to effectively predict 3D structure from sequence will likely assist in www.sciencedirect.com
Computational approaches in nucleic acid therapeutics Sherman and Contreras 237
this effort, potentially identifying targets leading to cancer-specific treatments, orphan disease therapies, and effective precision medicine. Finally, advancements in computational approaches that enable genome-wide identification of disease characteristics could provide early detection of diseases to induce lifestyle changes for longer, healthier livelihoods.
Funding Welch Foundation [F-1756]; Funding for open access charge: Welch Foundation [F-1756]; National Science Foundation CAREER Program [CBET-1254754].
Acknowledgements We would like to thank Dr. Elebeoba May for careful reading of the document and providing feedback and Daniel Herrera for assistance with data analysis. We would also like to thank K. Alexandra Krippner for assisting with illustrations.
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ScienceDirect Computational approaches in design of nucleic acid-based therapeutics Mark Sherman1 and Lydia Contreras2 Recent advances in computational and experimental methods have led to novel avenues for therapeutic development. Utilization of nucleic acids as therapeutic agents and/or targets has been recently gaining attention due to their potential as high-affinity, selective molecular building blocks for various therapies. Notably, development of computational algorithms for predicting accessible RNA binding sites, identifying therapeutic target sequences, modeling delivery into tissues, and designing binding aptamers have enhanced therapeutic potential for this new drug category. Here, we review trends in drug development within the pharmaceutical industry and ways by which nucleic acid-based drugs have arisen as effective therapeutic candidates. In particular, we focus on computational and experimental approaches to nucleic acidbased drug design, commenting on challenges and outlooks for future applications. Addresses 1 Cell and Molecular Biology Graduate Program, University of Texas at Austin, 100 E. 24th Street, A6500, Austin, TX 78712, USA 2 McKetta Department of Chemical Engineering, University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA Corresponding author: Contreras, Lydia ([email protected])
Current Opinion in Biotechnology 2018, 53:232–239 This review comes from a themed issue on Pharmaceutical biotechnology Edited by Amanda Lewis and Nripen Singh
https://doi.org/10.1016/j.copbio.2017.12.001 0958-1669/ã 2018 Published by Elsevier Ltd.
Introduction Over almost 80 years of FDA regulation, the field of drug discovery and development has transformed its focus from pain management of disease symptoms to systematic identification of causative factors and targeted mediation (Figure 1). Early pharmaceutical development stemmed from the isolation and concentration of active agents from natural products by chance discovery. Although the therapeutic mechanisms of these substances were unclear at the time, most contemporary drug development efforts aim to target specific disease-associated proteins. The majority of approaches involve screening small molecule Current Opinion in Biotechnology 2018, 53:232–239
libraries for target protein inhibition that lead to therapeutic effects. While these efforts have resulted in hundreds of new drug entities, the journey to approval for the average drug is increasingly costly [1]. The rising cost of drug development can, in part, be attributed to increased screening efforts due to low hit rates and to misdirected investigations based on proteins that show no interactions with current candidate drug molecule libraries; other reasons include the high probability of failure during the scale-up process and the escalating demand by regulatory bodies for more safety and efficacy data prior to approval [1,2]. Furthermore, many diseases simply cannot be influenced by small molecules due to the inability to identify a binding pocket in key proteins underlying disease pathways to serve as a suitable drug target. In addition, challenges to small molecules arise from the possibility that disease phenotypes are caused by mechanisms upstream of protein production, such as improper gene splicing, without a suitable small molecule target. In recent years, drug development efforts have become more sophisticated, relying less on large-scale screening of chemical compounds and more on targeted development of drugs toward specific molecular entities. Modern drug development pipelines attempt to target a wider spectrum of molecules beyond traditional enzyme targets, including metabolites [3], protein–protein interactions [4], and nucleic acids [5]. These advances have enabled treatment of various diseases through the development and engineering of new vaccines (with over 30 active vaccine-associated clinical trials currently underway (clinicaltrials.gov)), peptide therapeutics such as Parsabiv [6], nucleotide prodrugs such as Vosevi [7], as well as a variety of antibody-based technologies including antibody–drug conjugates (used in Mylotarg) [8,9], phage display (used in development of Humira) [10,11,12], and radiolabelled diagnostic antibodies as used in Netspot [13–15].
Incentives and challenges to nucleic-acid therapeutic development While protein-based therapeutics (antibodies, recombinant proteins, and peptides) have made up over a quarter of FDA approved drugs since 2015 (FDA.gov), they have relatively a short shelf life, require refrigerated transport, and rely on live organisms for production, making manufacturing them expensive, contamination prone, and variable across batches [16]. In response to these shortcomings, recent efforts have been made toward development of nucleic acid-based drugs, offering the key advantage of chemical manufacturing. As such, they www.sciencedirect.com
Computational approaches in nucleic acid therapeutics Sherman and Contreras 233
Figure 1
1939-1959 N= 27
1960-1999 N=111
1990-2017 N=357 Current Opinion in Biotechnology
An analysis of indications and usage descriptions from FDA approved drug labels across three time periods from 1939 to 2017. Between 1939 and 1959, approved drugs appear to be associated with pain management and symptomatic relief. Between 1960 and 1989, small molecule inhibitors appear to become mainstream and birth control is developed. Between 1990 and 2017, inhibition continued to be a major theme for pharmaceutical development and drugs became more specific toward particular diseases, including viruses such as HIV. During the last 30 years, the emergence of nucleic acids as targets (nucleosides) also represents a newer phenomenon. Data generated by an analysis on a collection of drug labels through U.S. National Library of Medicine’s DailyMed website.
maintain a relatively long half life, are easily transportable, are consistent from batch to batch, and have the potential to introduce new therapeutic chemistries [17] to the current, inhibitor-dominated field (Figure 1). For instance, DNA has been identified as a target for drug development [18] for its gene therapy ability via manipulation of gene expression or editing of defective genes [19]. Likewise, RNA variants have been implicated in a variety of disease phenotypes such as Spinal Muscular Atrophy (SMA) [20], Duchenne Muscular Dystrophy (DMD) [21,22], Alzheimer’s Disease, and cancer [23,24]. Therefore, development of nucleic acid-based drugs hold promise to provide high affinity and high specificity entities for therapeutic applications; however, progress in this field has been limited by challenges in delivery to specific tissues, a very active area of research (discussed later) [25–31]. In 2016, three nucleic acid-based therapies gained FDA approval: Exondys-51 [22] (for treatment of DMD), Spinraza [20] (for treatment of SMA), and Defitelio [32] (for treatment of renal or pulmonary dysfunction). Both Spinraza and Exondys-51 are antisense oligonucleotides that therapeutically influence splicing patterns of diseased proteins. The mechanism for Defitelio has not been elucidated [32]. Several other RNA-based or RNA-targeting drugs are currently in clinical trials (Table 1). The majority of these candidate drugs involve www.sciencedirect.com
infusion of chimeric antigen receptor (CAR) T cells while others are siRNA (small interfering RNA), shRNA (small hairpin RNA), and mRNA (messenger RNA) based. CAR T cells are derived from the patients own T cells which are genetically modified ex vivo and introduced back into the body with enhanced therapeutic functions. Currently approved RNA-based drugs are administered as local injections or systemic infusions (Table 1). As research in nucleic acid-based therapeutics progresses, it holds promise to develop drugs that can not only inhibit action of a protein, but could alter gene expression and metabolic flux through development of aptamers (RNA analogs of antibodies), prodrugs, siRNAs, miRNAs, and new vaccines with more efficient modes of delivery.
Current strategies toward aptamer-based therapeutic development Due to their ability to bind to diverse targets such as ions, dyes, amino acids, RNAs, oligosaccharides, antibodies, and cells, aptamers have become the main focus of nucleic acid-based therapeutic development [33]. Aptamers are short, single-stranded DNA or RNA oligonucleotides that bind to a variety of target molecules with high affinity and specificity, dependent on their tertiary structure [34]. The majority of DNA and RNA aptamers are experimentally developed via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) Current Opinion in Biotechnology 2018, 53:232–239
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Table 1 Nucleic acid-based drugs recently approved or currently in clinical trials Current phase
Active ingredient/indicated study
Indicated condition
Mode of delivery
Intervention
Early Non-Viral, RNA-Redirected Phase 1 Autologous T Cells
Hodgkin Lymphoma
Intravenous (IV) infusion
Early Phase 1 Phase 1 Phase 1 Phase 1
Autologous T Cells Expressing MET scFv CAR (RNA CART-cMET) RNActive1 Rabies Vaccine (CV7201) Hypoxia-inducible Factor 1a (HIF1A) Pbi-shRNATM EWS/FLI1 Type 1 LPX
Malignant Melanoma Breast Cancer Rabies Carcinoma, Hepatocellular Ewing’s Sarcoma
Intravenous (IV) infusion
Phase 1
Neutral Liposomal Small Interfering RNA Delivery of EphA2 cMet CAR RNA T Cells
Advanced Cancers
Intravenous (IV) infusion
CD19 RNA redirected autologous T-cells (RNA CART19 cells) T cells modified with RNA anti-cMET CAR mRNA mRNA Antagonist pbi-shRNATM EWS/FLI1 Type 1 LPX siRNA-EphA2-DOPC
Metastatic Breast Cancer Triple Negative Breast Cancer Malignant Neoplasms Brain
Intratumor administration
cMet RNA CAR T cells
Intravenous (IV) infusion
RNA-loaded dendritic cell vaccine; Basiliximab
Sickle Cell Disease
Intravenous (IV) infusion
Phase 1
Phase 1
Intravenous (IV) injection Intravenous (IV) infusion Intravenous (IV) infusion
Phase 1
Basiliximab in Treating Patients With Newly Diagnosed Glioblastoma Multiforme Undergoing Targeted Immunotherapy and Temozolomide-Caused Lymphopenia Gene Transfer
Phase 1
CD19+ CAR T Cells
Leukemia Lymphoma
Intravenous (IV) infusion
Phase 3
Dendritic Cells loaded with Autologous Tumor RNA
Uveal Melanoma
Intravenous (IV) infusion
Approved
Nusinersen (Spinraza)
Intrathecal injection
Approved
Eteplirsen (Exondys 51)
Intravenous (IV) infusion
Antisense Oligonucleotide
Approved
Defibrotid (Defiteli)
Spinal Muscular Atrophy (SMA) Duchenne muscular dystrophy (DMD) Severe hepatic venoocclusive disease (sVOD)
CD34+ HSC cells transduced with lentiviral vector containing shRNA targeting BCL11a Fludarabine monophosphate; Cyclophosphamide Autologous Dendritic Cells loaded with autologous Tumor RNA Antisense Oligonucleotide
Intravenous (IV) infusion
Oligonucleotide
Data collected from FDA Novel Drug Approval summaries (FDA.gov) and ClinicalTrials.gov.
[35,36], a process that utilizes multiple rounds of ligand selection and amplification of RNA or DNA library variants to raise aptamers with high affinity and selectivity to a target ligand. Aptamers can bind in the pM range of affinity and exhibit higher specificity and lower off-target effects than their protein counterparts [37]. The utilization of SELEX has contributed greatly in our understanding of RNA biology and development of nucleic acidbased therapeutics; however, its successful application to aptamers with desirable properties requires 10–20 rounds of selection, representing weeks to months of development effort [16]. While the coupling of SELEX with other molecular technologies such as high throughput sequencing, microarrays and bioinformatics have shown a reduction in selection efforts [38–41], computational modeling approaches for the rational design of aptamers, particularly RNA aptamers, show promise to greatly reduce development time and associated costs [16]. Current Opinion in Biotechnology 2018, 53:232–239
Role of computational approaches in aptamer-based therapeutic design The structural makeup of aptamers are key for their use as effective therapeutics [33,34]; therefore, development of structure prediction algorithms represents a significant undertaking in the rational design of aptamer therapeutics. Structural predictions from nucleic acid sequences are challenging due to structural differences observed in vivo versus in vitro [42], a high number of transitional states [43], and the influence of metal ions, other RNAs, small organic molecules, and proteins on 3D RNA structure [33,34,44]. To address these challenges, bioinformatics and computational modeling approaches have been constructed to statistically analyze existing datasets to increase the likelihood of successful aptamer design. In particular, databases of RNA-RNA interactions [45], RNA-DNA interactions [46], and nucleic acid-protein interactions [47] have been compiled to assist in these efforts [16]. www.sciencedirect.com
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In this scheme, once a target for the aptamer has been identified, structural data of both the target and the ideal aptamer is gathered or generated. This is generally done through crystallography-based approaches [48], homology modeling using modeling software like ModeRNA [49– 51], or through the use of a thermodynamics-based model such as RNAup [52] (Table 2). Furthermore, several models have been introduced in recent years to derive secondary structure from primary RNA sequence such as mFold [53] and ViennaRNA [54] (Table 2). In general, these models take into account a series of user defined constraints to predict RNA secondary structure through energy minimization algorithms. Models have also been developed to predict 3D tertiary structure from secondary structure (MC-FOLDjMC-Sym [55]) and even directly from primary sequence using coarse grained molecular dynamics approaches (NAST [56], ERNWIN [57]) (Table 2). A graphical summary of these models based on complexity is shown in Figure 2. For modeling RNA– RNA interactions of uncharacterized RNAs, a desirable metric is the identification of accessible regions of target RNAs in vivo [50]. To address this challenge, models have been developed and benchmarked to investigate the likelihood for RNA–RNA binding. These include IntaRNA [58], an RNA–RNA prediction algorithm based on seed constrains, and InTherAcc [43], an informed biophysical model based on biological data collected in vivo capturing RNA accessibility for binding using an in vivo RNA Structural Sensing System [42]. To help inform crystal structure determination and provide experimental validation for models, several biochemical techniques have been useful; these include hydroxyl radical footprinting (probing for protected nucleotides), selective 20 OH acylation by primer extension (SHAPE) (chemical probing for backbone flexibility), and dimethysulfate sequencing (probing for H bonding) [59].
Once tertiary structure is established, models can be utilized to predict docking location and strength of binding of nucleic acids and their targets based on atomistic simulations. These simulations calculate minimum free energy based on parameters such as electrostatic interactions, Van der Waals forces, and hydrophobic interactions. Atomistic modeling tools such as CHARMM [60] and AMBER [61] have been used in this fashion to simulate RNA–metal interactions and several examples of this approach have recently been reviewed in Sun et al. [50]. For instance, molecular simulations and Markov state modeling have been applied to predict the kinetics of RNA conformational state changes, supporting experimental observations that the binding of specific RNAs can be relatively slow and involves large conformational rearrangements after initial binding [62]. Understanding these dynamics can allow for efficient use of informatics tools toward rational design of therapeutic aptamers, a process analogous to approaches used in protein engineering for therapeutics [63].
Rational design of nucleic acid therapeutic delivery systems While the molecular toolkit of nucleic acid-based drugs is being diversified and potential therapeutic mechanisms are being elucidated, the challenge to effectively deliver these therapeutic agents to the site of treatment remains a major hurdle. That is, for a therapeutic molecule to reach its target site, several aspects need to be addressed; a few key ones include prevention of immunogenicity, escape from degradation enzymes, penetration through cellular barriers that inhibit uptake, release from liposomes before degradation, and (depending on its mechanism) transport to the nucleus to perform intended functions [64,65]. While nucleic acid-based drugs have been experimentally optimized for individual elements [64], there is not yet an
Table 2 A collection of modeling programs referenced in this article, their URL address, and a short description of their function Model
Website
ModeRNA MC-FOLDjMC-Sym NAST
http://genesilico.pl/moderna/ http://www.major.iric.ca/MC-Fold/ https://simtk.org/projects/nast
ERNWIN IntaRNA
http://rna.tbi.univie.ac.at/ernwin http://rna.informatik.uni-freiburg.de/IntaRNA/ Input.jsp http://rna.tbi.univie.ac.at/cgi-bin/ RNAWebSuite/RNAup.cgi http://unafold.rna.albany.edu/?q=mfold https://www.tbi.univie.ac.at/RNA/
RNAup mFold ViennaRNA AutoDock Vina HADDOCK PatchDock
http://vina.scripps.edu/ http://www.bonvinlab.org/software/haddock2. 2/haddock-start/ https://bioinfo3d.cs.tau.ac.il/PatchDock/
Description Comparative modeling of RNA 3D structures Predicts secondary structures from sequence Uses coarse grained molecular dynamics and a knowledge-based force field to generate RNA structures Predicts RNA 3D structure using a coarse-grain helix-centered model Prediction of interactions between two RNA molecules Predicts RNA-RNA interactions Predicts nucleic acid folding and hybridization A C code library and stand-alone programs for prediction and comparison of RNA secondary structures Molecular docking program to calculate free energy scores High Ambiguity Driven Biomolecular Docking based on Biochemical and/or biophysical information Molecular Docking Algorithm Based on Shape Complementarity Principles
Data collected from model-associated publications and their web portals.
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Figure 2
Current Opinion in Biotechnology
Graphical depiction of nucleic acid-based models and their uses across three levels of complexity: 2D RNA structure prediction from primary sequence, prediction of RNA–RNA interactions, and prediction of RNA–protein interactions. Data collected from model-associated publications and their web portals.
effective vehicle for targeted drug delivery. Current therapeutic delivery relies on transport of naked particles or suboptimal modes of delivery, such as PEGylation and liposomal based delivery [27,66]. Multi-scale computational approaches to these issues could complement experimental methods. Indeed, these approaches have been implicated both as tools to gain mechanistic insights on biological properties and as inspirations to develop novel systems for nanoparticle delivery [27]. Specifically, the coarse grained molecular dynamics simulation model, MARTINI [67], has been used to model RNA interactions with other biomolecules and has been implicated for use in nanoparticle-mediated delivery modeling [27,68,69].
distant future, it is possible that genetically engineered replacement organs could be delivered overnight for transplantation the following day [71]. The societal outcome of these revolutions may transition the role of health care from one of fixing health issues to one of surveillance followed by immediate treatment in response to detection of early disease indicators. Nucleic acid-based technologies, such as viral gene therapy and genome editing, enable the potential for permanent removal of diseases without sustained medication nor drug development for treatment of disease phenotypes. This type of therapy could reduce health care costs and could curtail the impact of complex diseases worldwide.
Conclusions Future directions of nucleic acid-based therapeutics Developments in DNA and RNA sequencing technologies, genome editing capabilities, and bioinformatics approaches hold promise to replace dependence of a comfortable lifestyle on pharmaceuticals with advanced control of gene expression, particularly for complex diseases caused by genetic anomalies [70]. As computational technologies become inextricably incorporated into society, their role in biotechnology and therapeutics is likely to expand. In the near future, these new technologies raise the possibility that a drop of blood placed on a device similar to a glucose monitor could screen the genome, transcriptome, proteome and metabolome for abnormalities and those anomalies could be medicated appropriately before the onset of disease. In a perhaps not so Current Opinion in Biotechnology 2018, 53:232–239
Nucleic acid-based therapeutics are quickly becoming relevant within the pharmaceutical industry. With three oligonucleotide based drugs approved in 2016 and over 40 privately in development [72] (ClinicalTrials.gov), the nucleic acid-based therapeutic industry appears to be gathering momentum. Additionally, increasing attention by the academic research community toward new tools and delivery mechanisms for DNA and RNA oligonucleotides suggests that we are not far off from advances in delivery to specific cell types and tissues, providing a new suite of therapeutic chemistries with fewer side effects and more efficient modes of action, rivaling antibodybased therapeutics. The development of computational tools to identify target molecules (DNA, RNA, protein, small molecules) and modeling approaches to effectively predict 3D structure from sequence will likely assist in www.sciencedirect.com
Computational approaches in nucleic acid therapeutics Sherman and Contreras 237
this effort, potentially identifying targets leading to cancer-specific treatments, orphan disease therapies, and effective precision medicine. Finally, advancements in computational approaches that enable genome-wide identification of disease characteristics could provide early detection of diseases to induce lifestyle changes for longer, healthier livelihoods.
Funding Welch Foundation [F-1756]; Funding for open access charge: Welch Foundation [F-1756]; National Science Foundation CAREER Program [CBET-1254754].
Acknowledgements We would like to thank Dr. Elebeoba May for careful reading of the document and providing feedback and Daniel Herrera for assistance with data analysis. We would also like to thank K. Alexandra Krippner for assisting with illustrations.
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