IDENTIFYING POTENTIAL OPPORTUNITIES FOR ......IDENTIFYING POTENTIAL OPPORTUNITIES FOR EMERGING TECHNOLOGIES BY USING LITERATURE LINKAGES Jing Ma1, Alan L. Porter2, 3, Tejraj M. Aminabhavi4

5th International Conference on Future-Oriented Technology Analysis (FTA) - Engage today to shape tomorrow Brussels, 27-28 November 2014

THEME 3: CUTTING EDGE FTA APPROACHES

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IDENTIFYING POTENTIAL OPPORTUNITIES FOR EMERGING

TECHNOLOGIES BY USING LITERATURE LINKAGES

Jing Ma1, Alan L. Porter2, 3, Tejraj M. Aminabhavi4

1. School of Management and Economics, Beijing Institute of Technology, Beijing, China, [email protected]

2. School of Public Policy, Georgia Institute of Technology, Atlanta, GA, USA, [email protected]

3. Search Technology, Inc., Atlanta, GA, USA

4. Department of Pharmaceutical Engineering and Chemistry, Soniya College of Pharmacy, Dharwad, India, [email protected]

Abstract

Technology Opportunities Analysis (TOA) seeks to generate useful information on developmental prospects relating to rapidly changing science and technology. This practical Future-oriented Technology Analysis (FTA) modality applies various empirical analyses in conjunction with expert interpretation. It can help scientists, R&D managers, and Science, Technology, and Innovation (ST&I) policy-makers identify opportunities to pursue. Herein we offer a systematic approach to summarize biomedical research information compiled from the MEDLINE database. By arraying selected technical dimensions against each other, we enable exploration for less-explored opportunities. We demonstrate the approach for Nano-Enabled Drug Delivery (NEDD), keying on three dimensions – nano components, treatment agents, and disease targets (cancers).

Keywords: Technology opportunities analysis; Tech mining; MEDLINE; Nano-enabled drug delivery; Cancers

Introduction

Various analysts are exploring opportunities to promote technological advances in a select field of interest (Olsson, 2005). Such analyses can support decision-making process for researchers, R&D planners and managers, and science policy-makers (Hsu et al., 2006; Lee et al., 2014). Pharmaceuticals entail a challenging, but high-payoff, set of rapidly emerging technologies. By finding and filling technological gaps, new solutions can be designed, which would ultimately accelerate ST&I development.

Networks of ideas, researchers, and inventors reflect in publications and patents as useful markers of ST&I development (Porter and Cunningham, 2005). In order to explore technology opportunities, researchers need to monitor dynamics to grasp the rising hotspots in a specific field by developing Competitive Technical Intelligence (CTI). To tackle these issues, bibliometrics and patent analysis draw upon an array of empirical and expert knowledge means to enable TOA. This protocol facilitates dealing with the massive volumes of information to generate viable intelligence (Yoon, 2008).

There are still some shortcomings. Social scientists as outsiders and technology watchers rely much on domain experts to improve the effectiveness of analysis, but this is a time-consuming process. We seek to develop means to expedite analyses, reducing the burden on experts, to provide timely and novel TOA.



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The present study presents a semi-automatic approach to identify potential technological opportunities using MEDLINE data. Herein, we apply this framework to Nano-Enabled Drug Delivery (NEDD), a rapidly emerging technology with potential to affect pharmaceutical practices dramatically. Our first approach is to focus on NEDD for various types of cancer treatment, taking full advantage of MEDLINE data structures. We then statistically generate topical clusters that offer perspective on key dimensions of NEDD R&D. By evaluating linkages across clusters, we strive to locate hotspots of activity, as well as gaps. The suggested framework is expected to be applicable to most life science and biomedical fields that will inspire researchers to explore some new or not fully explored combinations of drug delivery components, treatment agents, and targets.

We illustrate matrix generation for technological clusters and evaluating the linkages across clusters for the NEDD case. We believe this approach contributes to Future-oriented Technology Analysis (FTA) by deepened empirical analysis of widely available ST&I information resources.

1. Background

1.1 Tech Mining and Technology Opportunities Analysis (TOA)

In tech mining, we try to understand technological innovation progress by applying data mining and text mining tools to ST&I information resources (Porter and Cunningham, 2005). TOA builds upon such analytical capabilities.

TOA was first proposed in the 1990s as a framework combining monitoring and bibliometric analyses (Porter and Detampel, 1995) to provide insights into a specific technology as a value-added process and a detailed target of tech mining. The TOA process is distinguished by

several main steps (Zhu and Porter, 2002): ①search and retrieve text information, ②profile the

resulting search set, ③ extract latent relationships, ④ represent relationships graphically, and ⑤

interpret the prospects for successful technological development.

Based on this notion, many studies have been conducted to discover technology opportunities from different perspectives. Yoon and Park (2005) and Yoon et al. (2014) proposed a systematic approach of combining morphological analysis (MA) and text mining to explore technology opportunities by linking patents and products. They divided the technology opportunities into three levels: existing, applied, and heterogeneous. Recently, Guo et al. (2012) and Ma et al. (2014) analysed technology opportunities for dye-sensitised solar cells (DSSC) employing a technology delivery system (TDS) model. TDS modelling reflects the essential organizational players and environmental influences to carry R&D efforts to market. Other approaches, like patent mapping (Lee et al., 2009) and TRIZ (Zhang et al., 2014), have also been explored to facilitate TOA. It thus appears feasible to track recent historical technology development trends in TOA, with limited success at forecasting future developments. It is also difficult to delve into technological detail (e.g., subsystem roles), since such deeper analyses demands more domain knowledge. This prompts us to explore and locate technology opportunities in more detail by drawing on database indexing to capture more clues.

1.2 MEDLINE Data and MeSH

MEDLINE (Medical Literature Analysis and Retrieval System Online) is the premier database covering the biomedical domain (MEDLINE, 2014). It is compiled by the United State National Library of Medicine (NLM) and is available online via PubMed and also through Web of Knowledge. MEDLINE provides a high quality, hierarchical topical indexing system, which provides a controlled vocabulary – the MeSH (Medical Subject Headings) terms are both general



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and detailed. The NLM professionals who assign MeSH terms to articles have access to their full text. Each article can be indexed with several MeSH terms, but only some of them reflect the major points of the article. For these MeSH terms, they will be marked with an asterisk (Indexing with MeSH Vocabulary, 2014).

MeSH is considered the pivotal resource to capture research topics pertaining to a specific medical disease or condition. Leydesdorff et al. (2012) and Wallace and Rafols (2014) have applied MeSH to characterize research landscapes from the perspective of bibliometrics. Boyack et al. (2011) compared different approaches for clustering biomedical publications using MEDLINE data. Bornmann et al. (2008) suggest that MeSH terms are more appropriate classifiers than other journal groupings to measure research performance and impact in medical domains. On a macro level, MeSH terms can help researchers identify landscapes for a specific medical topic. This study seeks to take a more micro approach, using MeSH for detailed identification of research opportunities.

1.3 Nano-Enabled Drug Delivery (NEDD)

Nano-Enabled Drug Delivery (NEDD) is an emerging technology that engages several distinct domains, including biomedicine, nanotechnology, chemistry, and pharmacy. NEDD components have been successfully used as clinical tools to modulate drug release and to target particular diseased tissues (Felice et al., 2014). Advances in nanotechnology have enabled the development of nanoparticulate drug-delivery vehicles using various nanomaterials and components (mainly polymers). These systems have the ability to encapsulate, carry, and penetrate through biological membranes to deliver the payload (therapeutics) to specific target disease sites (Rudzinskj and Aminabhavi, 2010; Wang et al., 2012; Chaturvedi et al., 2011; Ganguly et al., 2014). Controlled, targeted delivery, and protecting therapeutics from premature degradation, makes NEDD a really promising approach to revolutionize disease treatment. NEDD-aided targeting of drug and gene treatments enables otherwise impossible deliveries, while greatly reducing side-effects as treatments, such as chemotherapy, blast other cells and organs.

Hitherto, many different kinds of nanoparticles have been developed as drug carriers, such as liposomes, micelles, polymeric conjugates and so on (Egusquiaguirre et al., 2012; Felice et al., 2014; Mundargi et al., 2008). Probably the most important application of NEDD has been the delivery of cancer treatments (Heidel and Davis, 2011; Wang et al., 2012; Aminabhavi et al., 2014). In this paper, our main objective is to construct linkages between different technological means and cancer therapeutics to identify novel opportunities.

2. Methodological Approach

2.1 Research Process

We address NEDD as an empirical tool for cancer treatment. While doing so, we follow several steps as displayed in Fig. 1, based on properties of the empirical field and data sources. The first critical step is to devise a sound search strategy and retrieve useful data. In this step, several specialists help us check and refine our search strategy.



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Fig. 1. Research process

Our data source is MEDLINE. We name those MeSH terms with asterisks as “primary MeSH terms” (MeSH-P) and for all MeSH terms assigned to an article, with or without an asterisk, we just name the field as “MeSH terms” (MeSH). Accompanying the MeSH terms, there are 83 topical qualifiers, such as administration and dosage, chemistry, and pathology; these are used to clarify how a particular MeSH term is addressed in a research article. Each MeSH term can have several qualifiers, and, also, qualifiers for the same MeSH terms in different articles can be different. The basic structure of MEDLINE indexing is shown in Fig. 2. In this study, the MeSH terms are the basic topical information we use, especially MeSH-P, instead of a combination of title and abstract Natural Language Processing (NLP) phrases that we have used elsewhere in analysing the NEDD research profiles based on Web of Science fundamental research and Derwent Innovation Index patent data (Zhou et al., 2014; Ma and Porter, 2014).

Fig. 2. MEDLINE indexing structure

Based on this structure, our third step is to cluster the MeSH terms into different sectors and then connect different clusters by building co-occurrence matrices [using Vantage Point text analysis software (www.theVantagePoint.com)]. The matrices help us identify linkages among

Search strategy and data collection

Acquiring topical information

Generating technological clusters

Creating matrices between different clusters

Evaluating linkages between clusters

Identifying hotspots and potential opportunities

http://www.thevantagepoint.com)/



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individual MeSH terms from different technological clusters. The most important research question in this study is how to evaluate these linkages in order to identify the vital and most promising ones – i.e., to perform TOA. Besides focusing on high frequencies, we calculate the relative research concentrations between different MeSH terms and by doing so, we are able to find some significant linkages, and provide these to NEDD specialists to identify the potential opportunities.

2.2 Clustering of Topical Keywords

Though we know the general aspects of how NEDD works, it is impossible to classify those MeSH terms into different sectors manually. We have performed several trials on how to generate technological clusters based on multi-source keywords and how to clean the data to improve the quality of topical clustering (Ma and Porter, 2014). For most of these studies, the topical clusters are generated based on co-occurrence using different algorithms. In our study, this approach is not applicable since we need to cluster MeSH terms based on their meaning or definition instead of their co-occurrence. For example, Doxorubicin and Breast Neoplasms often appear together, but we cannot cluster them into the same group as they belong to totally different domains – i.e., the first is a treatment drug and the second is a form of cancer.

We are using a new idea to cluster the MeSH terms by taking advantage of qualifiers. We build vectors for MeSH terms using qualifiers as their properties, and then calculate the cosine similarity between any two MeSH terms. The similarity is calculated as

Similarity(A, B) = ∑ 𝐴𝑖×𝐵𝑖

𝑛𝑖=1

√∑ (𝐴𝑖)2𝑛𝑖=1 ×√∑ (𝐵𝑖)2𝑛

𝑖=1

. (1)

Here, 𝐴𝑖 and 𝐵𝑖 stand for the frequency of a given term accompanied with qualifier i; 𝑛 stands for the number of qualifiers we use as properties.

2.3 Linkage Evaluation

By clustering MeSH terms into different topics, we can analyse several different aspects of the compiled research abstract set. Then by linking MeSH terms from different clusters, we try to figure out which link is more important in terms of a specific MeSH term; this MeSH term may represent a detailed type of cancer or a specific nanoparticle. The linkages between MeSH terms from different clusters are displayed in matrices, as shown in Fig. 3.

Fig. 3. Matrix between Cluster 1 and Cluster 2

In Fig. 3, we take cluster 1 as the basic topic and cluster 2 as the one that needs to be evaluated. There are m MeSH terms in Cluster 1 and n MeSH terms in Cluster 2. The number of records

covered by MeSH term Ci is ni. The number of co-occurrences of MeSH term Ci with MeSH term Dj is vij. Here, why do we have to use co-occurrence? That is because when two MeSH

terms from different aspects appear in the same article, they likely come together to describe the



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main topical theme of the article. For instance, when Doxorubicin and Breast Neoplasms are assigned to index a given article, it means this paper may talk about using doxorubicin as a drug to treat breast cancer. Let’s designate Breast Neoplasms as Ci and Doxorubicin as Dj. However,

there may be many other agents (drugs, genetic treatments), as well as many other kinds of neoplasms. So how to evaluate whether doxorubicin is significant or special for breast neoplasm treatment? To do this, we borrow the notion of TF-IDF (Term Frequency – Inverse Document Frequency – i.e., a normalized indicator of how specialized the term is) and apply a similar formula to evaluate the relative research concentrations (RRC) between different MeSH terms.

First, we want to know of all Dj hits in cluster 1, what proportion (pij) are directed at each MeSH

term? Secondly, MeSH terms in cluster 1 are studied independently, but not equally (qi), so we use term frequency as a weight to prevent bias towards MeSH terms that appear in more records. This allows us to calculate RRC:

RRCij = pij × log1

qi=

vij

∑ vhjmh=1

× log∑ nk

mk=1

ni, i = 1, 2, … , m, j = 1, 2, … , n. (2)

RRC operationalizes how special MeSH term Dj is to Ci. Also, by analysing how RRC changes

over time, we are able to identify hotspots and emergence (e.g., perhaps, recently, doxorubicin is being studied for a different cancer).

3. Search strategy and data collection

In this study, we are using Web of Knowledge’s MEDLINE interface for 2000 to 2013. Since the data from 2014 are still incomplete and many records haven’t been indexed, we search data up through 2013. Considering the properties of MEDLINE data, we develop a search strategy (see Table 1) mainly based on MeSH with 10354 records.

Table 1. Search strategy

# Search Strategy (Web of Knowledge’s MEDLINE 2000-2013, performed on 7/24/14) No. of

records

#1 MeSH (expanded): (Neoplasms or Antineoplastic Agents) AND (Drug carriers OR Micelles) 8,715

#2

MeSH (expanded): (Neoplasms or Antineoplastic Agents) AND (Nanostructures)

exclude records in #1, and refine with MeSH (Drug Delivery Systems, RNA Small Interfering, Gene

Transfer Techniques, Delayed-Action Preparations, RNA Interference, Pharmaceutical Vehicles,

Genetic Vectors, Transfection, Polyglycolic Acid)

1,517

#3

MeSH (expanded): (Neoplasms or Antineoplastic Agents) AND (Nanostructures)

exclude records in #1 and #2, and refine with MeSH (Doxorubicin, Polyethylene Glycols,

Paclitaxel) and description of drug delivery in title and abstract (deliver*)

122

#4 Total 10,354

To conduct this study, precision of data retrieval is very important. There are three components

in our search strategy: ①target (cancer/neoplasm), ②nanoparticle (drug carrier or targeting

moiety), and ③ delivery. We identify two key MeSH terms for targets -- Neoplasms and

Antineoplastic Agents. For nanoparticles, we use MeSH terms such as Drug Carriers, Micelles, and Nanostructures. Delivery is an optional part in this search strategy. When we use the combination of target and nanostructures to search, instead of NEDD, we find some irrelevant records about imaging, causing cancer, and other topics. To solve this problem, we use several MeSH terms to refine data retrieval, including Drug Delivery Systems, Gene Transfer



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Techniques, Delayed-Action Preparations, and so on. Furthermore, we apply “deliver*” in titles and abstracts to augment MeSH terms. We compare several common words, which can be used as filters like deliver, carrier, and vector. Here, deliver offers the best performance. The final dataset can be divided into three parts, as shown in Fig. 4.

Fig. 4. Sketch of search strategy

4. Illustration

4.1 Topical Clusters in NEDD

In this NEDD-cancer dataset, there are 5363 distinguished MeSH terms in the MeSH field and 3334 MeSH terms in the MeSH-P field. There are many generic terms in MeSH, including humans, animals, and so on, which are not representative and NEDD-specialized. The top 200 terms in MeSH-P cover 10211 records while 1415 MeSH terms only appear once. To generate technological clusters and build linkages, we use the top 200 terms in MeSH-P. The 73 qualifiers are identified in this dataset and the top 20 qualifiers cover 10289 records. We choose those top 20 qualifiers as properties to build vectors for the top 200 terms in MeSH-P (see Fig. 5).

Cancer/Neoplasms and Drug Carriers

#1 – 8715

Cancer/Neoplasms

and Nanostructures

#2 – 1517

#3 – 122

10354

Refine with MeSH

Refine with MeSH and delivery



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Fig. 5. Vectors for the top 200 terms in MeSH-P (partial)

Using SPSS, we calculate the similarity between each two terms and cluster the top 200 MeSH-P terms into seven clusters. We name the seven clusters based on their major terms:

Cancer – different types,

Agent – drugs and formulations,

Carrier component – nanoparticles, accompanying agents, and ligand,

Method – techniques, procedures, and programs,

Target effect – effects of treatment,

Biointerface – receptors and metabolism,

Antibody – antibodies and antigens.

For the 200 terms, only four of them – viz., Gene Transfer Techniques, Microspheres, Micelles, and Drug Design cannot be clustered in these seven topics automatically, because they have barely been assigned with qualifiers. So based on a manual review, we classify them into different clusters manually, which are method, component, component and method seperately. For the cluster cancer, to make it clearer, four generic cancer headings are excluded (Neoplasms; Neoplasms, Experimental; Carcinoma; Neoplasm Recurrence, Local), while others are combined based on the organ systems (see Table 2).

Table 2. Clusters of the Top 200 MeSH-P terms

Cluster MeSH terms

Cancer (original)

Neoplasms; Breast Neoplasms; Lung Neoplasms; Brain Neoplasms; Liver Neoplasms; Ovarian

Neoplasms; Glioma; Neoplasms, Experimental; Carcinoma, Hepatocellular; Prostatic

Neoplasms; Colonic Neoplasms; Melanoma, Experimental; Adenocarcinoma; Melanoma;



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Neovascularization, Pathologic; Pancreatic Neoplasms; Carcinoma, Squamous Cell;

Glioblastoma; Colorectal Neoplasms; Carcinoma; Mammary Neoplasms, Experimental; Liver

Neoplasms, Experimental; Skin Neoplasms; Stomach Neoplasms; Carcinoma, Non-Small-Cell

Lung; Head and Neck Neoplasms; Urinary Bladder Neoplasms; Peritoneal Neoplasms;

Osteosarcoma; Leukemia; Bone Neoplasms; Leukemia, Myeloid, Acute; Precursor Cell

Lymphoblastic Leukemia-Lymphoma; Neuroblastoma; Uterine Cervical Neoplasms; Carcinoma,

Lewis Lung; Lymphoma; Neoplasm Recurrence, Local; Sarcoma

Cancer

(combined)

Breast Neoplasms; Brain and Nerve Neoplasms; Liver Neoplasms; Lung Neoplasms; Skin

Neoplasms; Colorectal Neoplasms; Ovarian Neoplasms; Prostatic Neoplasms;

Adenocarcinoma; Leukemia; Pancreatic Neoplasms; Carcinoma, Squamous Cell; Stomach

Neoplasms; Bone Neoplasms; Head and Neck Neoplasms; Urinary Bladder Neoplasms;

Peritoneal Neoplasms; Uterine Cervical Neoplasms; Lymphoma; Sarcoma

Agent

Antineoplastic Agents; Doxorubicin; Antibiotics, Antineoplastic; Antineoplastic Agents,

Phytogenic; Paclitaxel; RNA, Small Interfering; Antineoplastic Combined; Chemotherapy

Protocols; Camptothecin; Antimetabolites, Antineoplastic; Cisplatin; DNA; Taxoids; Antibodies,

Monoclonal; Fluorouracil; Photosensitizing Agents; Prodrugs; Angiogenesis Inhibitors;

Curcumin; Genetic Vectors; Oligonucleotides, Antisense; Daunorubicin; Methotrexate;

Cytarabine; Organoplatinum Compounds; Deoxycytidine; Plasmids; Porphyrins; Vincristine;

Peptide Fragments; Antineoplastic Agents, Alkylating; Organometallic Compounds;

Radiopharmaceuticals; Amphotericin B; Mitoxantrone; Tretinoin; Antifungal Agents;

Immunoconjugates; Oxides; Etoposide; Adjuvants, Immunologic; Indoles; Tamoxifen;

Pharmaceutical Preparations; Antineoplastic Agents, Hormonal; Radiation-Sensitizing Agents;

Tumor Necrosis Factor-alpha; Oligodeoxyribonucleotides, Antisense; Vinblastine;

Anthracyclines; Epirubicin

Carrier

component

Drug Carriers; Nanoparticles; Liposomes; Polyethylene Glycols; Polymers; Micelles;

Nanocapsules; Nanostructures; Dendrimers; Lipids; Chitosan; Peptides; Lactic Acid; Polyesters;

Polyglycolic Acid; Folic Acid; Metal Nanoparticles; Gold; Delayed-Action Preparations;

Oligopeptides; Magnetite Nanoparticles; Silicon Dioxide; Biocompatible Materials; Nanotubes,

Carbon; Contrast Media; Ferric Compounds; Polyethyleneimine; Surface-Active Agents;

Polyamines; Hyaluronic Acid; Phosphatidylethanolamines; Transferrin; Poloxamer; Fluorescent

Dyes; Quantum Dots; Hydrogels; Cholesterol; Nanoconjugates; Dextrans; Phospholipids;

Methacrylates; Serum Albumin; Proteins; Nanotubes; Peptides, Cyclic; Polymethacrylic Acids;

Acrylic Resins; Nanospheres; beta-Cyclodextrins; Microspheres; Vitamin E; Acrylamides;

Nanocomposites; Aptamers, Nucleotide; Polyglactin 910; Polyglutamic Acid; Cell-Penetrating

Peptides; Graphite; Cyclodextrins; Phosphatidylcholines; Polylysine; Coated Materials,

Biocompatible

Method

Drug Delivery Systems; Genetic Therapy; Gene Transfer Techniques; Transfection;

Nanomedicine; Nanotechnology; Photochemotherapy; Magnetic Resonance Imaging;

Magnetics; Hyperthermia, Induced; Drug Design; Molecular Imaging; Immunotherapy;

Diagnostic Imaging; Drug Compounding; Chemoembolization, Therapeutic; Technology,

Pharmaceutical; Molecular Targeted Therapy; Boron Neutron Capture Therapy; Ultrasonics;

Microscopy, Fluorescence; Chemistry, Pharmaceutical

Target effect Apoptosis; Drug Resistance, Neoplasm; RNA Interference; Cell Proliferation; Drug Resistance,

Multiple; Cell Survival; Gene Silencing; Endocytosis; Gene Expression Regulation, Neoplastic

Biointerface

Receptor, Epidermal Growth Factor; Carrier Proteins; Receptor, erbB-2; Receptors, Cell

Surface; Macrophages; Cell Membrane; Liver; Vascular Endothelial Growth Factor A; Neoplasm

Proteins; P-Glycoprotein; Thymidine Kinase; Adenoviridae; Blood-Brain Barrier; Brain

Antibody Cancer Vaccines; Antigens, Neoplasm; Dendritic Cells; Antibodies



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The seven technological clusters are constructed automatically. The strength of this culstering result is that each of the seven clusters represents a section of the whole NEDD-cancer domain and the seven clusters build a general framework of NEDD-cancer (see Fig. 6). The first three clusters, cancer, agent, and carrier component constitute the kernel of NEDD for cancer treatment and in the next part of our study, we analyse the linkages among these three clusters and hunt for under-addressed opportunities. Before the next step, we double-check terms in these three clusters with our NEDD specialist and exclude several irrelevant ones.

Fig. 6. NEDD-cancer framework

4.2 Matrices and Linkages

Based on the seven clusters, we can have several combinations to build matrices. In this study, we have chosen the most important three clusters for two matrices and analysed their RRC. The two matrices are Cancer–Agent (see Fig. 7) and Cancer–Component. Also, to observe how RRC has changed, we calculate RRC of each cell for every year; the RRC is accumulated. The RRC for Doxorubicin to Breast Neoplasms in 2004 is calculated with records from 2000-2004. Most RRC scores should be on a downtrend. Because when some new agent, component, or material is introduced and is applied to some kind of cancer, it becomes significant, and may be a breakthrough. But with time, it may be used more widely and its concentration on a given cancer will tend to drop. If the RRC for one agent/carrier component directed to treat some kind of cancer keeps on an uptrend, or remains stable, it means the agent continues to remain in the spotlight for addressing this cancer type.

Cancer Site

Biointerface

Target Effect

Carrier

Antibody/Ligand

Agent

Method



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Fig. 7. Cancer-Agent-2013

Fig. 8 shows the average RRC of agents and carrier components directed to cancer treatment. Both trends support our hypothesis of RRC’s decline. In 2000, the average RRC of components was much higher than that of agents, while in 2013, they are almost the same, but the RRC of components was slightly lower. The fall of components is steeper, which means the generalization of components’ application is more common. Even though the pathology varies, it should be feasible for most agents or components to be applied to multiple cancer targets, especially for nanoparticles and nanomaterials. If these MeSH-P terms are indexed accurately and effectively, the trend in Fig. 8 illustrates that treatment of different cancer types borrows ideas and notions from each other on the application of agents and carrier components.

Fig. 8. Average RRC of Cancer-Agent and Cancer-Component

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

RR

C

Year

Cancer-Agent

Cancer-Component



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4.3 Identifying Potential Opportunities

By analysing the RRC of different agents and components for specific kinds of cancers, we try to find hotspots and gaps (potential opportunities) from different perspectives. To illustrate how we conduct this process, here we focus on one specific type – Brain and Nerve Neoplasms (BNN). In the final part of this process, we present several alternatives and candidates to NEDD domain specialists for them to check the utility and effectiveness of the results.

Tables 3 and 4 provide the top 15 agents and carrier components for BNN from two aspects. Number of records illustrates an absolute value of linkage, and RRC is more about specialization. In Table 3, we can find many common antineoplastic agents are also often used for BNN, like Doxorubicin. However, its RRC is relatively low for BNN. Even though the absolute number of articles on the use of doxorubicin for BNN is considerable, it is also widely used for most cancer types addressed here. However, doxorubicin is connected with breast neoplasms in MeSH-P in 142 articles, and with liver neoplasms in 85 articles. From the perspective of RRC for BNN, the most significant agent is “Antineoplastic Agents, Alkylating.” It is also in the top 15 list of number of records. “Antineoplastic Agents, Alkylating” is related to several popular BNN agents, including Carmustine and Temozolomide.

Table 3. Top 15 agents for BNN in No. of records and in RRC (2013)

Top 15 Agents in # Records # Records RRC Top 15 Agents in RRC # Records RRC

Antineoplastic Agents 91 0.12 Antineoplastic Agents, Alkylating 14 0.50

Doxorubicin 57 0.09 Methotrexate 4 0.19

Paclitaxel 35 0.13 Peptide Fragments 4 0.16

Antineoplastic Agents, Phytogenic 30 0.11 Angiogenesis Inhibitors 6 0.14

RNA, Small Interfering 24 0.10 Oligonucleotides, Antisense 10 0.14

Antineoplastic Combined

Chemotherapy Protocols 15 0.06 DNA 13 0.13

Antineoplastic Agents, Alkylating 14 0.50 Antibodies, Monoclonal 11 0.13

DNA 13 0.13 Paclitaxel 35 0.13

Antibodies, Monoclonal 11 0.13 Genetic Vectors 5 0.12

Oligonucleotides, Antisense 10 0.14 Antineoplastic Agents 91 0.12

Angiogenesis Inhibitors 6 0.14 Antineoplastic Agents, Phytogenic 30 0.11

Genetic Vectors 5 0.12 Antineoplastic Agents, Hormonal 1 0.11

Cisplatin 4 0.04 RNA, Small Interfering 24 0.10

Curcumin 4 0.08 Doxorubicin 57 0.09

Methotrexate 4 0.19 Prodrugs 2 0.09

For delivery-aiding components, transferrin (acts as the ligand) is the most specialized component for BNN. Drugs have to pass through the Blood Brain Barrier (BBB) to reach brain cell targets (Pardridge, 1998; Agnihotri et al., 2004; Roney et al., 2005), and transferrin supports this process. Polylactic acid nanoparticles may also have this advantage, so that we can see Lactic Acid in both top 15 lists too. Cell-Penetrating Peptides are No. 2 in the top 15 RRC list.



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The first time it has been connected with BNN in MeSH-P indexing was 2013, which makes it a newcomer. According to its relatively high RRC for BNN, it hasn’t been widely used for other cancer types. Its emergence may reflect some important characteristics of this material.

Table 4. Top 15 carrier components for BNN in No. of records and in RRC (2013)

Top 15 Components in # Records #

Records RRC Top 15 Components in RRC

#

Records RRC

Nanoparticles 90 0.14 Transferrin 13 0.42

Drug Carriers 77 0.12 Cell-Penetrating Peptides 2 0.30

Liposomes 37 0.08 Nanoconjugates 5 0.22

Nanocapsules 34 0.16 Cyclodextrins 2 0.22

Polyethylene Glycols 30 0.11 Peptides, Cyclic 4 0.20

Polymers 18 0.12 Microspheres 4 0.20

Lactic Acid 16 0.18 Biocompatible Materials 6 0.19

Nanostructures 16 0.13 Nanospheres 3 0.19

Dendrimers 14 0.15 Lactic Acid 16 0.18

Peptides 13 0.11 Aptamers, Nucleotide 3 0.17

Polyglycolic Acid 13 0.16 Peptides 13 0.16

Transferrin 13 0.42 Nanocapsules 34 0.16

Oligopeptides 9 0.11 Polyglycolic Acid 13 0.16

Contrast Media 8 0.14 Dendrimers 14 0.15

Micelles 7 0.06 Nanotubes, Carbon 7 0.15

Besides these “top” agents/components, some emerging and low-frequency actors warrant consideration. Cisplatin is a common antineoplastic agent that has been widely used for many cancer types, like ovarian neoplasms and lung neoplasms. The only two cancer types with which cisplatin is not involved are leukemia and lymphoma (at the level of MeSH-P). Some pathology concerns may hold back its application in these two cancer types. For BNN, it started from 2010 and only has 4 articles. But the RRC of cisplatin to BNN has been increasing since then (see Fig. 9). Thus, even though it is not on those top lists, its emergence still needs some attention. For ovarian neoplasms, cisplatin can be combined with nanocapsules, dendrimers and micelles. But to cisplatin for BNN, these were not identified in MeSH-P. Thesecomponents may also be applicable to cisplatin for BNN treatment.



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Fig. 9. RRC of cisplatin to major cancer types

To sum up from different perspectives, we can find many significant or emerging points, as can be seen in Table 5. Based on this BNN case, we can screen and evaluate these linkages and classify them into different categories. If we treat every MeSH term as a technical point, then for those that have been deeply explored for other targets, they may be introduced to some new targets. For those emerging agents or materials, they may be combined with some traditional components to develop a new delivery platform. This entire process of building and evaluating matrices would help researchers to clarify doubts about the mysteries in a specific field in a visual manner and to help in the next stage of exploration.

Table 5. Different Potentials based on Linkages

# No. of

Records RRC Notation Examples

1 High High High specialization and high frequency. Deeply

explored. Core technical detail.

Antineoplastic Agents,

Alkylating; Transferrin

2 High Low High frequency and widely applied. Common

applications. Doxorubicin

3 Low High Emerging point. Not widely developed. Cell-Penetrating

Peptides

4 Low Low If appearing early, it seems not very relevant. But if

newly appearing, this may be a candidate successor. Cisplatin

In order to avoid the overcrowding of the data presentation, we could not list all the matrices and combination possibilities. However, examination of other combinations of the seven factors noted in this study would make much sense. The matrix between carrier components and agents helps scientists identify particular nanocarriers that might be used to deliver drugs for which they

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5R

RC

Year

Ovarian Neoplasms

Lung Neoplasms

Carcinoma, Squamous Cell

Liver Neoplasms

Pancreatic Neoplasms

Prostatic Neoplasms

Skin Neoplasms

Colorectal Neoplasms

Adenocarcinoma

Brain and Nerve Neoplasms



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have not been attempted. Also, the matrix of components vs. biointerface may inspire researchers to consider some new approaches to deliver drugs through different biological barriers or routes. Furthermore, with use of software tools (e.g., Vantage Point), we can introduce third or even fourth dimensions so as to reveal multi-level details and linkages. For instance, we can select a cell and check which organizations are publishing most actively on that topic.

5. Conclusions

In modern pharmaceutical disciplines, nano-delivery approaches have received wide attention. Efforts to understand disease mechanisms and to find an effective treatment-drug combination, as well as to identify technological opportunities, constitute important components of modern R&D management. This has prompted researchers and inventors not only to keep up with R&D trends, but also to monitor advancing technological capabilities in “adjacent” domains that could inform or even transform these areas. We present a systematic process to analyse and explore particular technological potentials in biomedical areas, especially taking advantage of the structure and topical information of MeSH indexing. The process requires modest specialist involvement to check search strategy and interpret results.

We illustrate this process for NEDD-cancer treatment. By generating technological clusters, we are able to construct the general framework of NEDD-cancer research. Furthermore, we are able to build matrices between different technological dimensions to assess the presence or absence of linkages between delivery components, treatment agents, and target elements. From the standpoint of technology watchers, we try to provide researchers the ability to capture technology developmental dynamics.

In two workshops on our NEDD analyses (one at Georgia Tech and one at a Drug Delivery conference in Texas), we were advised to pursue more micro-level tech mining. That is, while overall patterns and trends are interesting, researchers and R&D managers seek more detailed technical intelligence. The approach presented here is a response to this charge. In particular, the exploration of the “BNN” R&D pattern illustrates the potential to locate specific “next opportunities.” We envision such R&D profiling as vital, for instance, for graduate students looking for cutting edge dissertation topics.

This process depends on the use of MeSH indexing; we have made a concerted effort to adapt this process to different kinds of data sources, especially less structured text content. We work with text analytics to extract topical intelligence from title and abstract fields to enrich the MeSH-P analyses. Our RRC measure adds a perspective to the basic co-occurrence frequency, enabling an analyst to note changing research thrusts. The linkages are displayed here in two-dimensional relationship, but in the software, we explore third-dimensions of interest interactively. We further seek to provide an effective way to provide such multi-dimensional relationship exploration to contribute to ST&I decision-making processes.

Acknowledgements

We acknowledge support from the US National Science Foundation, Science of Science & Innovation Policy (SciSIP) Program (Award No. 1064146 – “Revealing Innovation Pathways: Hybrid Science Maps for Technology Assessment and Foresight”) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Award No. 13YJC630042). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation and MOE. The authors would like to thank Xiao Zhou, Douglas K.R. Robinson, Ying Guo, and Min Suk Shim for their contributions to the NEDD analyses. We also thank our colleagues of the Innovation Co-lab from



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Beijing Institute of Technology, Georgia Tech, and the University of Manchester for their feedback.

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IDENTIFYING POTENTIAL OPPORTUNITIES FOR ......IDENTIFYING POTENTIAL OPPORTUNITIES FOR EMERGING TECHNOLOGIES BY USING LITERATURE LINKAGES Jing Ma1, Alan L. Porter2, 3, Tejraj M. Aminabhavi4