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. 2022 Nov 3;15(11):1357.
doi: 10.3390/ph15111357.

Data-Driven Technology Roadmaps to Identify Potential Technology Opportunities for Hyperuricemia Drugs

Lijie Feng  1 , Weiyu Zhao  2 , Jinfeng Wang  3 , Kuo-Yi Lin  4 , Yanan Guo  5   6 , Luyao Zhang  7   8
Affiliations

Data-Driven Technology Roadmaps to Identify Potential Technology Opportunities for Hyperuricemia Drugs

Lijie Feng et al. Pharmaceuticals (Basel). .

Abstract

Hyperuricemia is a metabolic disease with an increasing incidence in recent years. It is critical to identify potential technology opportunities for hyperuricemia drugs to assist drug innovation. A technology roadmap (TRM) can efficiently integrate data analysis tools to track recent technology trends and identify potential technology opportunities. Therefore, this paper proposes a systematic data-driven TRM approach to identify potential technology opportunities for hyperuricemia drugs. This data-driven TRM includes the following three aspects: layer mapping, content mapping and opportunity finding. First we deal with layer mapping.. The BERT model is used to map the collected literature, patents and commercial hyperuricemia drugs data into the technology layer and market layer in TRM. The SAO model is then used to analyze the semantics of technology and market layer for hyperuricemia drugs. We then deal with content mapping. The BTM model is used to identify the core SAO component topics of hyperuricemia in technology and market dimensions. Finally, we consider opportunity finding. The link prediction model is used to identify potential technological opportunities for hyperuricemia drugs. This data-driven TRM effectively identifies potential technology opportunities for hyperuricemia drugs and suggests pathways to realize these opportunities. The results indicate that resurrecting the pseudogene of human uric acid oxidase and reducing the toxicity of small molecule drugs will be potential opportunities for hyperuricemia drugs. Based on the identified potential opportunities, comparing the DNA sequences from different sources and discovering the critical amino acid site that affects enzyme activity will be helpful in realizing these opportunities. Therefore, this research provides an attractive option analysis technology opportunity for hyperuricemia drugs.

Keywords: SAO analysis; data-driven TRM; human uric acid oxidase; hyperuricemia drug; link prediction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Structure of technology roadmap.
Figure 2
Figure 2
Research framework.
Figure 3
Figure 3
The data-driven TRM for the technology layer. The technical layer was divided into six communities represented by C1, C2, C3, C4, C5, and C6. Dashed cycles with different colors highlight diverse communities. The edges’ width and arrows’ width represent the probability of a potential link between unconnected nodes. The wider the edges and arrows, the higher the likelihood of a potential link. Arrows and edges with different colors represent different community themes.
Figure 4
Figure 4
Statistical results of papers, patents, and commercial data related to hyperuricemia drugs.
Figure 5
Figure 5
Topic coherence curve for technology layer. (A) Topic coherence value of S components for technology layer in 2010–2013 (T-S-TS1). (B) Topic coherence value of S components for technology layer in 2014–2018 (T-S-TS2). (C) Topic coherence value of S components for technology layer in 2019–2021 (T-S-TS3). (D) Topic coherence value of A components for technology layer in 2010–2013 (T-A-TS1). (E) Topic coherence value of A components for technology layer in 2014–2018 (T-A-TS2). (F) Topic coherence value of A components for technology layer in 2019–2021 (T-A-TS3). (G) Topic coherence value of O components for technology layer in 2010–2013 (T-O-TS1). (H) Topic coherence value of O components for technology layer in 2014–2018 (T-O-TS2). (I) Topic coherence value of O components for technology layer in 2019–2021 (T-O-TS3).
Figure 6
Figure 6
Topic coherence curve for market layer. (A) Topic coherence value of S components for market layer in 2010–2013 (M-S-TS1). (B) Topic coherence value of S components for market layer in 2014–2018 (M-S-TS2). (C) Topic coherence value of S components for market layer in 2019–2021 (M-S-TS3). (D) Topic coherence value of A components for market layer in 2010–2013 (M-A-TS1). (E) Topic coherence value of A components for market layer in 2014–2018 (M-A-TS2). (F) Topic coherence value of A components for market layer in 2019–2021 (M-A-TS3). (G) Topic coherence value of O components for market layer in 2010–2013 (M-O-TS1). (H) Topic coherence value of O components for market layer in 2014–2018 (M-O-TS2). (I) Topic coherence value of O components for market layer in 2019–2021 (M-O-TS3).
Figure 7
Figure 7
The data-driven TRM for the market layer. The marketing layer was divided into five communities represented by C7, C8, C9, C10, and C11. Dashed cycles with different colors highlight diverse communities. The edges’ width and arrows’ width represent the probability of a potential link between unconnected nodes. The wider the edges and arrows, the higher the likelihood of a potential link. Arrows and edges with different colors represent different community themes.
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