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All Journal VALENSI JPTK: Jurnal Pendidikan Teknologi dan Kejuruan INOTEKS : Jurnal Inovasi Ilmu Pengetahuan, Teknologi, dan Seni Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Akademika Kimia Journal of Islamic Economic Laws Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Pertanian Darajat: Jurnal Pendidikan Agama Islam Best Journal (Biology Education, Sains and Technology) Fitrah: Jurnal Studi Pendidikan Jurnal Kebidanan Malakbi Jurnal Studi Jurnalistik FOUNDASIA Mejuajua Jurnal Farmanesia Journal Of Human And Education (JAHE) Journal of Tropical Biodiversity Jurnal Sehat Indonesia (JUSINDO) Dharma: Jurnal Pengabdian Masyarakat International Journal of Religion Education and Law Indonesian Journal of Medical Chemistry and Bioinformatics Cendikia: Jurnal Pendidikan dan Pengajaran Jurnal ABDIMAS MUTIARA JEMARI : Jurnal Edukasi Mahasiswa Sunan Giri Bima Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Mahasiswa & Penelitian Keperawatan JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL
Siti Nurbaya
Mahasiswa S1 Prodi PGSD
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In Silico Analysis of Tea Leaf Compounds Targeting Inflammatory Pathways and Acne-Related Genes Sumarlin, La Ode; Nurbaya, Siti; Wulandari, Meyliana; Sulaiman, Syed Azhar Syed
Jurnal Kimia Valensi Jurnal Kimia VALENSI, Volume 11, No. 2, November 2025
Publisher : Department of Chemistry, Faculty of Science and Technology Syarif Hidayatullah Jakarta State Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jkv.v11i2.46468

Abstract

Acne (Acne vulgaris) is a chronic skin disease affected by Cutibacterium acnes infection and inflammatory pathways that trigger innate immune responses, such as inflammasome activation. The expression of inflammation-related genes plays a critical role in acne pathogenesis and immune modulation. This study aims to identify compounds from tea leaves (Camellia sinensis var. assamica) that can treat acne by influencing the expression of inflammatory-related genes through in silico analysis. The GSE6475 dataset was utilized to identify differentially expressed genes (DEGs) between acne-affected and normal skin samples (each group n=6). A total of 573 DEGs were identified and mapped to the KEGG inflammatory pathway. The hub gene analysis results showed six genes, including CXCL1, STAT1, and PIK3 (adj. P-value < 0.05). These key genes were then used to cross-validate skin grouping with acne lesions and normal skin. The structure of compounds (natural products) in tea leaves (C. sinensis var. assamica) was obtained from the PubChem database, and their activity against target proteins associated with the identified key genes was predicted using the SkelSpheres descriptor and Support Vector Regression method. This quantitative structure–activity relationship (QSAR)-based machine learning approach was selected because it enables high-throughput prediction of inhibitory potential using chemical descriptors and experimentally derived bioactivity data, providing broader predictive power than conventional molecular docking or molecular dynamics, which rely mainly on structural and energetic estimations. The in-silico prediction results showed that compounds such as theobromine, assamsaponin, procyanidin, and caffeine have exhibited good predicted activity (IC₅₀ ranging from 1.125 to 1.320 μM) as potential inhibitors of the PI3K/Akt pathway, which is known to play a role in the pathogenesis of acne. 
Co-Authors . Zulfan Adiansyah Adiansyah, Adiansyah Ahmad Gazali, Ahmad Aini, Yulis Nurul Albert Rufinus Allevia Yumnanisha, Defin Anisa Anisa Aryo Tedjo Asnuddin, Asnuddin asyir, alfiah Asyir, Alfiah Khoiri Ayu Sulasari Azhar Alam Baharuddin Hamzah Bambang Suryanto Baroroh Lestari Br Brahmana, Nettietalia Chandra, Devina Chotimah, Octavia Christianta, David Dalimunthe, Agusmal De Lux Putra, Effendy Derlina . Dicky Yuswardi Wiratma Dwi Astuti, Leny Ellyn Eka Wahyu Eriwaty Silalahi, Yosy Cinthya Ernawati Sinaga Evelina, Tri Yulistyawati fahima, sinaida Faruqi, Muhammad Fauzi Ahmad Muda Fitrah, Harisa Fitri, Raissa Fitriani Fitriani Ginting, Pitalokasari br Hasniati, Hasniati Hermawansyah Hermawansyah Hermiza Mardesci Heru Utomo, Heru Hutauruk, Dumartina Irianti Handayani, Supri Kholis Ridho, Kholis La Ode Sumarlin Laksono Trisnantoro Lestari, Wita Lumbantoruan, Lamtiur Manase, Pebrianti Manurung, Kesaktian Marpaung, Jon Kenedy Meyliana Wulandari Monica Suryani Muhammad Irfan MUHAMMAD TAUFIK Nasaruddin Nurfadillah, Ainaya Nurhalisa, Nurhalisa Nuriyanti Sirait Nurussakinah, Nurussakinah Pangaribuan, Novalina Perri, Perri Pratiwi, Aulia Febriyanti Priltuis, Natanael Puji Sari Purba, Trionaldo Purwandari, Vivi Purwantini, Mei Rahma, Salsabila Shofia Rahmah, Fiska Rejekinta Munthe, Alfian rubino, rubino Ruslan Safwan, Ahmad Gazali Samran, Samran Sapitri, Alfi Sembiring, Rinawati Setyawan Pujiono Sianipar, Artha Yuliana SIHOMBING, IWEL SUDARNO Silitonga, Maringan Simanjuntak, Kristina Simarmata, Yettrie Simatupang, Aprili Yanti Sitanggang, Berton Sitanggang, Eva Priasanti Sitanggang, Pontisari Siti Faridah Siti Maimunah Siti Nuryanti Sitompul, Desi Sitorus, Elly Sitorus, Elly Nurita Sitorus, Panal Situmorang, Winton F. Siva Fauziah, Siva Sofwan, Ahmad Gazali Sofyan . Suharyanisa Sukayasa Sumardi . Supartiningsih - - Supendi, Egih Suprianto Suprianto SURBAKTI, CHRISTICA ILSANNA Syahriani Syahriani, Syahriani SYAIFUL BAHRI Syed Azhar Syed Sulaiman, Syed Azhar Syed Tang, Muhammad Tarigan, Yenni Gustian Thaib, Cut Masyithah Tri Yulistyawati Evelina urai salam Veranita, Weri Vivitri Dewi Prasasty Wahyu, Ellyn Eka Yamin, Ashabul Yanti Musyawarah Yosy Cinthya Eriwaty Silalahi Yusra, Yusra Zainuddin, Zainuddin Zuhairiah Nasution, Zuhairiah