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Extraction, anti-tyrosinase, and antioxidant activities of the collagen hydrolysate derived from Rhopilema hispidum

Ab Aziz NA, Salim N, Zarei M, Saari N, Yusoff FM

Prep Biochem Biotechnol, 2021;51(1):44-53.
PMID: 32701046 DOI: 10.1080/10826068.2020.1789991

The study was conducted to determine anti-tyrosinase and antioxidant activities of the extracted collagen hydrolysate (CH) derived from Malaysian jellyfish, Rhopilema hispidum. Collagen was extracted using 1:1 (w:v) 0.1 M NaOH solution at temperature 25 °C for 48 hr followed by treatment of 1:2 (w:v) distilled water for another 24 hr and freeze-dried. The extracted collagen was hydrolyzed using papain at optimum temperature, pH and enzyme/substrate ratio [E/S] of 60 °C, 7.0 and 1:50, respectively. CH was found to exhibit tyrosinase inhibitory activity, DPPH radical scavenging and metal ion-chelating assays up to 64, 28, and 83%, respectively, after 8 hr of hydrolysis process. The molecular weight of CH was found <10 kDa consisting of mainly Gly (19.219%), Glu (10.428%), and Arg (8.848%). The UV-visible spectrum analysis showed a major and minor peak at 218 and 276 nm, accordingly. The FTIR spectroscopy confirmed the amide groups in CH. The SEM images demonstrated spongy and porous structure of CH. In the cytotoxicity study, CH has no cytotoxicity against mouse embryonic 3T3 fibroblast cell line with IC50 value >500 µg/ml. Results revealed that the CH generated from this study has a potential to be developed as active ingredient in cosmeceutical application.
Fulltext Ligand-based virtual screening using Bayesian networks

Abdo A, Chen B, Mueller C, Salim N, Willett P

J Chem Inf Model, 2010 Jun 28;50(6):1012-20.
PMID: 20504032 DOI: 10.1021/ci100090p

A Bayesian inference network (BIN) provides an interesting alternative to existing tools for similarity-based virtual screening. The BIN is particularly effective when the active molecules being sought have a high degree of structural homogeneity but has been found to perform less well with structurally heterogeneous sets of actives. In this paper, we introduce an alternative network model, called a Bayesian belief network (BBN), that seeks to overcome this limitation of the BIN approach. Simulated virtual screening experiments with the MDDR, WOMBAT and MUV data sets show that the BIN and BBN methods allow effective screening searches to be carried out. However, the results obtained are not obviously superior to those obtained using a much simpler approach that is based on the use of the Tanimoto coefficient and of the square roots of fragment occurrence frequencies.
Ocular co-morbidities in patients with atopic dermatitis - a cross-sectional study from a tertiary referral hospital, Malaysia

Gan TS, Juares Rizal A, Salim NL, Lau LL, Voo SYM

Med J Malaysia, 2022 Jan;77(1):6-11.
PMID: 35086988

INTRODUCTION: Atopic dermatitis (AD) is a chronic relapsing pruritic inflammatory skin disease that commonly occurs among children as well as adults. AD patients were reported to have high prevalence of ocular manifestations, which may be due to the disease nature or drug complications. This study aimed to determine the prevalence of ocular manifestations in patients with AD.
MATERIALS AND METHODS: Eighty patients who fulfilled the UK Working Party's Diagnostic Criteria for Atopic Dermatitis were included in the cross-sectional study. A standardized case report form was formulated to collect the demographic data and disease profile of the participants. AD severity was evaluated using the EASI and SCORAD score. All patients underwent a complete ophthalmological evaluation.
RESULTS: The prevalence of ocular manifestations among the patients with AD was 48.8%. Fifty-four (67.5%) patients had facial dermatitis and 37 (46.2%) showed periorbital signs. The mean AD disease duration was 10.99 ± 11.20 years. Majority of the patients had mild to moderate AD. The most frequent ocular manifestation was allergic conjunctivitis (18.75%) followed by cataract (8.75%) and ocular hypertension (8.75%). Among the patients with ocular manifestations, 27 (69.2%) patients regularly applied topical corticosteroids on the face. The use of systemic corticosteroids was seen in 19 (42.2%) patients. Prolonged AD duration was significantly associated with the development of ocular manifestations.
CONCLUSIONS: Nearly half of the patients with AD were complicated with ocular disease regardless of the AD severity, facial dermatitis and presence of periorbital signs. Long disease duration is associated with ocular manifestations, especially steroid related complications.
Coccidial infections of goats in Selangor, peninsular Malaysia

Jalila A, Dorny P, Sani R, Salim NB, Vercruysse J

Vet Parasitol, 1998 Jan 31;74(2-4):165-72.
PMID: 9561704

Coccidial infections were studied in goats in the state of Selangor (peninsular Malaysia) during a 12-month period. The study included 10 smallholder farms on which kids were monitored for faecal oocyst counts from birth until 1-year old. Eimeria oocysts were found in 725 (89%) of 815 faecal samples examined. Nine species of Eimeria were identified. The most prevalent were E. arloingi, found in 71% of the samples, E. ninakohlyakimovae (67%), E. christenseni (63%) and E. alijevi (61%). The other species found were, E. hirci, E. jolchijevi, E. caprovina, E. caprina and E. pallida, present in 34, 22, 12, 9 and 4% of the samples, respectively. Oocyst counts were significantly higher in animals of less than 4-months old (P < 0.05). High oocyst counts were mainly caused by non-pathogenic species. Poor hygienic conditions were found to be associated with a higher intensity of coccidial infections. Mortality rates in kids could not be related to the intensity of coccidial infections.
Investigation and Optimization of Hydrogel Microneedles for Transdermal Delivery of Caffeine

Chandran R, Mohd Tohit ER, Stanslas J, Salim N, Tuan Mahmood TM

Tissue Eng Part C Methods, 2022 10;28(10):545-556.
PMID: 35485888 DOI: 10.1089/ten.TEC.2022.0045

Caffeine is therapeutically effective for treating apnea, cellulite formation, and pain management. It also exhibits neuroprotective and antioxidant activities in different models of Parkinson's disease and Alzheimer's disease. However, caffeine administration in a minimally invasive and sustainable manner through the transdermal route is challenging owing to its hydrophilic nature. Therefore, this study demonstrated a transdermal delivery approach for caffeine by utilizing hydrogel microneedle (MN) as a permeation enhancer. The influence of formulation parameters such as molecular weight (MW) of PMVE/MA (polymethyl vinyl ether/maleic anhydride) copolymer and sodium bicarbonate (NaHCO3) concentration on the swelling kinetics and mechanical integrity of the hydrogel MNs was investigated. In addition, the effect of different MN application methods and needle densities of hydrogel MN on the skin insertion efficiency and penetration depth was also evaluated. The swelling degree at equilibrium percentage (% Seq) recorded for hydrogels fabricated with Gantrez S-97 (MW = 1,500,000 Da) was significantly higher than formulation with Gantrez AN-139 (MW = 1,080,000 Da). Increasing the concentration of NaHCO3 also significantly increased the % Seq. Moreover, a 100% penetration was recorded for both the applicator and combination of applicator and thumb pressure compared with only 11% for thumb pressure alone. The average diameter of micropores created by the applicator method was 62.94 μm, which was significantly lower than the combination of both applicator and thumb pressure MN application (100.53 μm). Based on histological imaging, the penetration depth of hydrogel MN increased as the MN density per array decreased. The hydrogel MN with the optimized formulation and skin insertion parameters was tested for caffeine delivery in an in vitro Franz diffusion cell setup. Approximately 2.9 mg of caffeine was delivered within 24 h, and the drug release profile was best fitted to the Korsmeyer-Peppas model, displaying Super Case II kinetics. In conclusion, a combination of thumb and impact application methods and reduced needle density improved the skin penetration efficiency of hydrogel MNs. The results also show that hydrogel MNs fabricated from 3% w/w NaHCO3 and high MW of copolymer exhibit optimum physical and swelling properties for enhanced transdermal delivery.
Fulltext Leptin induces the expression of tumorigenic genes in the gastric mucosa of male Sprague-Dawley rats

Isyraqiah F, K Kutty M, Durairajanayagam D, Salim N, Singh H

Exp Biol Med (Maywood), 2018 Oct;243(14):1118-1124.
PMID: 30449153 DOI: 10.1177/1535370218813909
The goat as a model for studies of pneumonic pasteurellosis caused by Pasteurella multocida

Zamri-Saad M, Effendy WM, Maswati MA, Salim N, Sheikh-Omar AR

Br. Vet. J., 1996 Jul;152(4):453-8.
PMID: 8791853

A model of pneumonic pasteurellosis has been established in goats using Pasteurella multocida harvested from pneumonic lungs of goats (types A and D), rabbits (type A) and sheep (type D). The resultant infections were acute, subacute or chronic. The gross and histological lesions of the subacute and chronic infections were typical of pneumonic pasteurellosis. P. multocida type D produced significantly (P < 0.01) more severe lesions when compared with other isolates. There were strong correlations between the clinical signs and the severity of lesions.
Fulltext CMD: A Database to Store the Bonding States of Cysteine Motifs with Secondary Structures

Bostan H, Salim N, Hussein ZA, Klappa P, Shamsir MS

Adv Bioinformatics, 2012;2012:849830.
PMID: 23091487 DOI: 10.1155/2012/849830

Computational approaches to the disulphide bonding state and its connectivity pattern prediction are based on various descriptors. One descriptor is the amino acid sequence motifs flanking the cysteine residue motifs. Despite the existence of disulphide bonding information in many databases and applications, there is no complete reference and motif query available at the moment. Cysteine motif database (CMD) is the first online resource that stores all cysteine residues, their flanking motifs with their secondary structure, and propensity values assignment derived from the laboratory data. We extracted more than 3 million cysteine motifs from PDB and UniProt data, annotated with secondary structure assignment, propensity value assignment, and frequency of occurrence and coefficiency of their bonding status. Removal of redundancies generated 15875 unique flanking motifs that are always bonded and 41577 unique patterns that are always nonbonded. Queries are based on the protein ID, FASTA sequence, sequence motif, and secondary structure individually or in batch format using the provided APIs that allow remote users to query our database via third party software and/or high throughput screening/querying. The CMD offers extensive information about the bonded, free cysteine residues, and their motifs that allows in-depth characterization of the sequence motif composition.
Fulltext Modeling and optimization of nanoemulsion containing Sorafenib for cancer treatment by response surface methodology

Izadiyan Z, Basri M, Fard Masoumi HR, Abedi Karjiban R, Salim N, Shameli K

Chem Cent J, 2017;11:21.
PMID: 28293282 DOI: 10.1186/s13065-017-0248-6

The aim of this study is the development of nanoemulsions for intravenous administration of Sorafenib, which is a poorly soluble drug with no parenteral treatment. The formulation was prepared by a high energy emulsification method and optimized by response surface methodology. The effects of overhead stirring time, high shear rate, high shear time, and cycles of high-pressure homogenizer were studied in the preparation of nanoemulsion loaded with Sorafenib. Most of the particles in nanoemulsion are spherical in shape, the smallest particle size being 82.14 nm. The results of the 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, a tetrazole reveal that the optimum formulation does not affect normal cells significantly in low drug concentrations but could remove the cancer cells. Finally, a formulation containing Sorafenib retained its properties over a period of 90 days. With characterization, the study of the formulated nanoemulsion has the potential to be used as a parenteral nanoemulsion in the treatment of cancer. Graphical abstractSchematic figure of high pressure homogenizer device.
Fulltext A new graph-based molecular descriptor using the canonical representation of the molecule

Hentabli H, Saeed F, Abdo A, Salim N

ScientificWorldJournal, 2014;2014:286974.
PMID: 25140330 DOI: 10.1155/2014/286974

Molecular similarity is a pervasive concept in drug design. The basic idea underlying molecular similarity is the similar property principle, which states that structurally similar molecules will exhibit similar physicochemical and biological properties. In this paper, a new graph-based molecular descriptor (GBMD) is introduced. The GBMD is a new method of obtaining a rough description of 2D molecular structure in textual form based on the canonical representations of the molecule outline shape and it allows rigorous structure specification using small and natural grammars. Simulated virtual screening experiments with the MDDR database show clearly the superiority of the graph-based descriptor compared to many standard descriptors (ALOGP, MACCS, EPFP4, CDKFP, PCFP, and SMILE) using the Tanimoto coefficient (TAN) and the basic local alignment search tool (BLAST) when searches were carried.
Fulltext Chemical named entities recognition: a review on approaches and applications

Eltyeb S, Salim N

J Cheminform, 2014;6:17.
PMID: 24834132 DOI: 10.1186/1758-2946-6-17

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.
Weighted voting-based consensus clustering for chemical structure databases

Saeed F, Ahmed A, Shamsir MS, Salim N

J Comput Aided Mol Des, 2014 Jun;28(6):675-84.
PMID: 24830925 DOI: 10.1007/s10822-014-9750-2

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.
Fulltext Ligand-based virtual screening using Bayesian inference network and reweighted fragments

Ahmed A, Abdo A, Salim N

ScientificWorldJournal, 2012;2012:410914.
PMID: 22623895 DOI: 10.1100/2012/410914

Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
Ligand expansion in ligand-based virtual screening using relevance feedback

Abdo A, Saeed F, Hamza H, Ahmed A, Salim N

J Comput Aided Mol Des, 2012 Mar;26(3):279-87.
PMID: 22249773 DOI: 10.1007/s10822-012-9543-4

Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
New fragment weighting scheme for the Bayesian inference network in ligand-based virtual screening

Abdo A, Salim N

J Chem Inf Model, 2011 Jan 24;51(1):25-32.
PMID: 21155550 DOI: 10.1021/ci100232h

Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.
Similarity-based virtual screening with a bayesian inference network

Abdo A, Salim N

ChemMedChem, 2009 Feb;4(2):210-8.
PMID: 19072820 DOI: 10.1002/cmdc.200800290

Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.
Fulltext Aspect extraction on user textual reviews using multi-channel convolutional neural network

Da'u A, Salim N

PeerJ Comput Sci, 2019;5:e191.
PMID: 33816844 DOI: 10.7717/peerj-cs.191

Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models.
Efficient Prediction of In Vitro Piroxicam Release and Diffusion From Topical Films Based on Biopolymers Using Deep Learning Models and Generative Adversarial Networks

Salma H, Melha YM, Sonia L, Hamza H, Salim N

J Pharm Sci, 2021 06;110(6):2531-2543.
PMID: 33548245 DOI: 10.1016/j.xphs.2021.01.032

The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm2 was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f2 values equal to 99.99 for all the formulations.
Fulltext Enhancement of physicochemical properties of nanocolloidal carrier loaded with cyclosporine for topical treatment of psoriasis: in vitro diffusion and in vivo hydrating action

Musa SH, Basri M, Fard Masoumi HR, Shamsudin N, Salim N

Int J Nanomedicine, 2017;12:2427-2441.
PMID: 28405165 DOI: 10.2147/IJN.S125302

Psoriasis is a chronic autoimmune disease that cannot be cured. It can however be controlled by various forms of treatment, including topical, systemic agents, and phototherapy. Topical treatment is the first-line treatment and favored by most physicians, as this form of therapy has more patient compliance. Introducing a nanoemulsion for transporting cyclosporine as an anti-inflammatory drug to an itchy site of skin disease would enhance the effectiveness of topical treatment for psoriasis. The addition of nutmeg and virgin coconut-oil mixture, with their unique properties, could improve cyclosporine loading and solubility. A high-shear homogenizer was used in formulating a cyclosporine-loaded nanoemulsion. A D-optimal mixture experimental design was used in the optimization of nanoemulsion compositions, in order to understand the relationships behind the effect of independent variables (oil, surfactant, xanthan gum, and water content) on physicochemical response (particle size and polydispersity index) and rheological response (viscosity and k-value). Investigation of these variables suggests two optimized formulations with specific oil (15% and 20%), surfactant (15%), xanthan gum (0.75%), and water content (67.55% and 62.55%), which possessed intended responses and good stability against separation over 3 months' storage at different temperatures. Optimized nanoemulsions of pH 4.5 were further studied with all types of stability analysis: physical stability, coalescence-rate analysis, Ostwald ripening, and freeze-thaw cycles. In vitro release proved the efficacy of nanosize emulsions in carrying cyclosporine across rat skin and a synthetic membrane that best fit the Korsmeyer-Peppas kinetic model. In vivo skin analysis towards healthy volunteers showed a significant improvement in the stratum corneum in skin hydration.
Fulltext Development of a kojic monooleate-enriched oil-in-water nanoemulsion as a potential carrier for hyperpigmentation treatment

Syed Azhar SNA, Ashari SE, Salim N

Int J Nanomedicine, 2018;13:6465-6479.
PMID: 30410332 DOI: 10.2147/IJN.S171532

Introduction: Kojic monooleate (KMO) is an ester derived from a fungal metabolite of kojic acid with monounsaturated fatty acid, oleic acid, which contains tyrosinase inhibitor to treat skin disorders such as hyperpigmentation. In this study, KMO was formulated in an oil-in-water nanoemulsion as a carrier for better penetration into the skin.
Methods: The nanoemulsion was prepared by using high and low energy emulsification technique. D-optimal mixture experimental design was generated as a tool for optimizing the composition of nanoemulsions suitable for topical delivery systems. Effects of formulation variables including KMO (2.0%-10.0% w/w), mixture of castor oil (CO):lemon essential oil (LO; 9:1) (1.0%-5.0% w/w), Tween 80 (1.0%-4.0% w/w), xanthan gum (0.5%-1.5% w/w), and deionized water (78.8%-94.8% w/w), on droplet size as a response were determined.
Results: Analysis of variance showed that the fitness of the quadratic polynomial fits the experimental data with F-value (2,479.87), a low P-value (P<0.0001), and a nonsignificant lack of fit. The optimized formulation of KMO-enriched nanoemulsion with desirable criteria was KMO (10.0% w/w), Tween 80 (3.19% w/w), CO:LO (3.74% w/w), xanthan gum (0.70% w/w), and deionized water (81.68% w/w). This optimum formulation showed good agreement between the actual droplet size (110.01 nm) and the predicted droplet size (111.73 nm) with a residual standard error <2.0%. The optimized formulation with pH values (6.28) showed high conductivity (1,492.00 µScm-1) and remained stable under accelerated stability study during storage at 4°C, 25°C, and 45°C for 90 days, centrifugal force as well as freeze-thaw cycles. Rheology measurement justified that the optimized formulation was more elastic (shear thinning and pseudo-plastic properties) rather than demonstrating viscous characteristics. In vitro cytotoxicity of the optimized KMO formulation and KMO oil showed that IC50 (50% inhibition of cell viability) value was >100 µg/mL.
Conclusion: The survival rate of 3T3 cell on KMO formulation (54.76%) was found to be higher compared to KMO oil (53.37%) without any toxicity sign. This proved that the KMO formulation was less toxic and can be applied for cosmeceutical applications.

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