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California law requires a business or state or local agency to notify any California resident whose unencrypted personal information, as defined, was acquired, or reasonably believed to have been acquired, by an unauthorized person. You can read the law here: California Civil Code s. Code s. The law also requires that a sample copy of a breach notice sent to more than California residents must be provided to the California Attorney General. Below is a list of those sample breach notices. Note that in some cases the organization that sent the notice is not the one that experienced the breach. For example, a bank may notify of a credit card number breach that occurred not at the bank, but at a merchant. You can search by the name of the organization that sent the notice, or simply scroll through the list. To read a notice, click on the name of the organization in the list. Then click on the link titled 'Sample Notification. Skip to main content. Search Search. Home About. Grants Grant Opportunities. Programs See All Programs. Search Data Security Breaches Home Privacy Search Data Security Breaches California law requires a business or state or local agency to notify any California resident whose unencrypted personal information, as defined, was acquired, or reasonably believed to have been acquired, by an unauthorized person. Organization Name:. Date of Breach Range: Date. Totally Promotional. Birth Choice of San Marco. Varsity Brands Inc. Tri-City Healthcare District. Wellfleet Group, LLC. Form I-9 Compliance. Gryphon Healthcare, LLC. MiCare Health Center. Bel-Air Bay Club Ltd. Omni Family Health. Kulicke and Soffa Industries. Fidelity Investments. Cottonwood Union School District. TheraCom, L. Calibrated Healthcare, LLC. AccessLex Institute. McKell Financial Group. Community HousingWorks. California Department of Social Services. Harvard Pilgrim Health Care. Advanced Sterilization Products, Inc. Truist Bank. Empereon Marketing. 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Official websites use. Share sensitive information only on official, secure websites. Determining the mechanism of action MOA of novel or naturally occurring compounds mostly relies on assays tailored for individual target proteins. Here we explore an alternative approach based on pattern matching response profiles obtained using cultured neuronal networks. Conolidine and cannabidiol are plant-derivatives with known antinociceptive activity but unknown MOA. To verify this, Cav2. Remarkably, conolidine and cannabidiol both inhibited Cav2. These data highlight the utility of cultured neuronal network-based workflows to efficiently identify MOA of drugs in a highly scalable assay. In many cases, determining mechanisms of action MOA of naturally occurring compounds has proven difficult using standard pharmacological and physiological approaches. Natural compounds can act on multiple targets and standard assays may lack sufficient biological complexity to report the impact of multi-target molecules or to be sensitive to the highly non-linear mechanisms that affect neuronal excitability 1. There is a clear need for workflows that can efficiently scale to the needs of central nervous system CNS drug discovery, assist in deconvoluting pharmacologic targets to help in understanding MOAs and efficiently predict in vivo efficacy and side effects or toxicity 2. Advances in instrumentation, pattern recognition and cell and molecular biology have converged to create new opportunities for the development of innovative drug discovery workflows. Cultures comprised of excitatory neurons, inhibitory neurons and glia self-assemble into spontaneously firing two-dimensional networks that share many functional and structural features with in vivo neuronal networks. A large number of neuronal network characteristics can be readily measured in these cultures using multi-electrode array MEA technology 3 — 5. For example, network scale analysis can reveal the impact of drugs on emergent behaviour that would not be possible in single cell assays. To this end, cultured neuronal networks grown on MEAs coupled with pattern recognition techniques to build a database of drug-response functional profiles has aided the classification and identification of the MOA of novel chemical entities 6 , 7. Here, we show how this approach can be used to identify the pharmacological target of conolidine and cannabidiol, two chemically divergent naturally occurring antinociceptive agents. Conolidine is an alkaloid derived from the stem bark of the tropical flowering shrub Tabernaemontana Divaricate Crepe Jasmine , which has been used in traditional Chinese, Thai and Ayurveda medicine for centuries for a range of ailments 8. Tarselli et al. Cannabidiol is another naturally-occurring compound with complex MOAs, which is already used clinically in the management of pain 10 , Numerous targets have been implicated 12 — 14 , although the precise MOA is still under active investigation. Our profiling and comparison of the actions of conolidine and cannabidiol with known pharmacological agents predicted a MOA shared with Ca v 2. In vitro analysis through whole cell patch clamping confirmed that, both conolidine and cannabidiol effectively blocked Ca v 2. Experiments were carried out with multiwell MEAs from Multichannel Systems with each multiwell plate containing 24 wells. Culture media was prepared with Laminin was removed immediately prior to plating. Cortical pieces, pooled from multiple mice were subjected to dissociation using 0. MEAs were covered with lids and kept in an incubator. HEKT cells were transiently transfected using the calcium phosphate precipitation method The transfection mixture included human Ca v 2. Each culture grown in a MEA well was considered as one sample. Drugs were selected using two main criteria: i known mode of action and ii potential clinical analgesic properties. The known molecular target s of the drugs used are outlined in Table 1. Cultures were incubated in the intermediary period between recordings. CVIE was provided by Prof. Paul F. Stock solutions of compounds were initially prepared using solvents given in Table 1. These were further diluted in media and added to cultures so that the final concentrations in the bath were as shown in Table 1. In the case of CVIE, morphine and bicuculline, high doses failed to produce significant reductions in mean firing and a concentration was selected based solely on literature analysis. Solvent concentration was kept at less than 0. Bursts in single channels single—channel bursts and network bursts were detected using an adaptive algorithm based on firing rates as described previously Bursts on single channels were detected as rapid successions of three or more spikes with inter-spike intervals lower than a threshold that adaptively changes based on firing rates. Detected spikes, bursts and network bursts are illustrated in Supplementary Fig. Network characteristics were extracted in terms of firing and bursting parameters. Average amplitude of spikes and mean firing rates were calculated for each channel and these channel-wise means were averaged across channels again. Burst features were calculated for both single-channel bursts and network bursts. Single-channel burst parameters include the number of spikes inside bursts and burst durations. Network burst parameter features consist of network burst durations, inter network burst intervals INBI-time interval between the end of a network burst and the beginning of the next network burst , the amplitude of spikes in a network burst averaged over the network burst duration avgNBAmp and network burst jitter Supplementary Fig. Jitter was defined as the onset time for channels that participated in the network burst. For burst parameters, the mean, coefficient of variation and range were calculated. The network burst rate and the average number of spikes in a network burst were also calculated. A full list of parameters and their definitions are included in Supplementary Table 1. For each parameter the percentage change from baseline was calculated. As data acquisition methods and feature extraction methods advance, the analysis of multiparametric data becomes an absolute necessity. Multiparametric data analysis methods are common in fields such as bioinformatics where dimensionality reduction methods are used to reduce vast numbers of features into a few useful features 21 , Such analysis has also been used in the context of MEAs to a certain extent 4 , Therefore, we employ dimensionality reduction methods to reduce the complexity of multiparametric MEA data and compare responses of MEA cultures to different compounds. For each sample, the percentage changes in parameters form a vector that describes its change in activity. To compare a drug to a set of drugs with known MOA, feature values from all samples were first z-scored and PCA 24 was performed on the set of feature vectors. Z-scoring ensures that all features have unit variance which prevents PCA from assigning higher importance to features with high variance. Principal components are orthogonal to each other, therefore using principal components overcomes the problem of extracted features being correlated to each other, which would otherwise bias similarity calculations between drugs. Each principal component describes a percentage of the variance of the data set and principal components are ordered according to this so that the first principal component describes the largest percentage of variance. Averages were calculated from the extracted principal component scores corresponding to the samples of each drug, resulting in an average vector per drug. MDS maps high-dimensional feature vectors into a lower dimensional space in a way that the dissimilarities between pairs of points are retained as much as possible. Dissimilarities were calculated as Euclidean distances between average feature vectors of drugs. The similarity between a pair of drugs was calculated as the Euclidean distance between their positions in the final two-dimensional space. In all cases, paired t-tests were performed to calculate statistically significant differences in the absence and presence of a drug using Matlab a The MathWorks, Inc. These maps display multiple features of a particular drug as segments of a circle. Segments correspond to p-values resulting from statistical comparisons of the individual features in the absence and presence of a drug, and are represented by a log colour scale with red shades for increases in values and blue shades for decreases Supplementary Fig. P-values were adjusted for multiple comparisons using the Benjamini and Hochberg method Each iris plot serves as a signature for the responses evoked by a single drug and provides additional statistical insight that is separate from the comparison process involving PCA and MDS. Fire-polished borosilicate patch pipettes GCTF All compounds and reagents used to prepare extracellular and intracellular solutions were sourced from Sigma-Aldrich. Conolidine, cannabidiol and CVIE were added to the extracellular solutions using stock solutions prepared as described in Table 1. Current amplitudes obtained in the presence of a compound I were normalized to current amplitudes obtained under control conditions I max. Statistical analyses of patch-clamp data were performed using Sigma Plot The baseline properties of the 14—21 DIV neonatal cortical cultures were characterised. A total of 24 parameters were extracted and their ranges are shown in Supplementary Table 1. Network bursting across the majority of electrode channels was a consistent feature of these cultures and is a hallmark of network maturity Supplementary Fig. Another consistent feature of the network activity in these cultures was a prevalence of solitary spiking that occurred between network bursts. The consistency of network properties in these cultures provided a quantifiable framework on which to analyse the properties of drugs and test compounds. Average values of each network property are given in Supplementary Table 1. This impact is reflected in quantifiable network features, including an increase in mean firing rates inside bursts relative to outside bursts MFRRatio and reduction in coefficients of variation in the duration of network bursts cvNBDur and the interval between network bursts cvINBI across 18 cultures. Each segment in the circle represents one feature. Cannabidiol also evoked synchronous, periodic network bursts with consistent durations and minimal solitary spiking. This is reflected as quantifiable network features in its iris plot Fig. Even though cannabidiol increased the regularity of network bursting compared to baseline conditions, it could not achieve the level of regularity that conolidine evoked. A major goal of the present study was to identify the biological targets for conolidine and cannabidiol by comparison of drug effects in MEA cultures. We selected nine central nervous system CNS active compounds given in Table 1 based on the range of different targets that they engage. Although this is a somewhat limited selection it represents a broad pharmacological space including, voltage gated channels, ionotropic and metabotropic receptors. Changes in network behaviour evoked by drugs used for comparison. In each sub plot, representative raster plots of baseline activity of a culture, the activity after drug application and an iris plot showing the statistically significant changes in network features are shown. In the iris plot, each segment in the circle represents one feature. Each drug caused distinct changes in the response profiles of the cultured neuronal networks Fig. For example, bicuculline, which is a GABA A receptor antagonist, had a visually similar response profile to conolidine and cannabidiol, with synchronous periodic network bursting that was less frequent and with negligible solitary spiking Fig. However, differences included increased firing rates and increased number of spikes in bursts. The response profiles of the five remaining drugs also showed distinct properties with similarities and differences to conolidine and cannabidiol Supplementary Fig. Overall visual analysis of raster and iris plots was not sufficient to rank drugs that most closely shared response profiles with conolidine and cannabidiol. Figure 4a shows the relationship of conolidine to other drugs on a two-dimensional plane created after multidimensional scaling showing strong separation of drug response profiles. The Euclidean distances between conolidine and each of the other drugs on the two-dimensional space are reported in Fig. Carbamazepine had the largest distance at 7. Bicuculline, though it had a visually similar activity profile, was positioned at a considerable distance from conolidine at 3. Similarity of conolidine to drugs with known mechanisms of action. Multidimensional scaling analysis was undertaken for cannabidiol Fig. Carbamazepine had the largest distance at 6. The distribution of individual sample points are included as Supplementary Figs 7 and 8. Similarity of cannabidiol to drugs with known mechanisms of action. In order to test the target predictions from our MEA workflow we tested the impact of conolidine and cannabidiol on neuronal Ca v 2. Conolidine and cannabidiol inhibition of currents through transiently expressed human Ca v 2. Bar indicates conolidine application. Representative inset I Ba traces in the absence and presence of conolidine are shown at the times indicated by lowercase letters and the red filled circle, respectively. Horizontal dotted line represents zero-current level. Data presentation and voltage protocol similar to that shown in a. Conolidine, applied at the same concentration used in MEA analyses, inhibited depolarization-activated whole-cell I Ba through Ca v 2. The effect developed slowly, occurred at physiologically relevant membrane potential V m values with no apparent change of the current-voltage I—V relationships, and showed poor reversibility Fig. Next, we examined whether cannabidiol could directly modulate Ca v 2. Figure 6d shows I Ba through Ca v 2. Cannabidiol reduced peak I Ba amplitude in a concentration and time-dependent manner Fig. This data argues strongly that both conolidine and cannabidiol inhibition of Ca v 2. Conolidine and cannabidiol are two naturally occurring compounds that have antinociceptive properties. Here we identify Ca v 2. We developed a workflow in which numerous firing parameters recorded on MEA are extracted from cultured neuronal networks and subjected to pattern recognition to identify similarity to known compounds. This workflow resulted in the nomination of Ca v 2. This provides a potentially powerful method through which the MOA of unknown compounds can be efficiently determined. Current workflows used for identifying the MOA of novel compounds generally involve an array of functional screening assays incorporating a myriad of isolated biological targets. These methods generally do not record function in the dynamic environment in which these targets usually reside. This is particularly important for ion channel targets that operate in very specialised and interactive temporal and spatial domains. The complexity of cultured networks provides a more realistic substrate on which to test CNS compounds. One difficulty of using this approach has been extracting and analysing unique signatures or response profiles for a given compound to create a similarity index. Existing methods that incorporate MEA recordings of cultured neuronal networks in their workflow rely largely on analysis of single parameters for identifying differences in drug action 29 , Because MEAs provide a richness of parameters, analyses that can incorporate the effects of a drug on all these parameters should be able to separate drugs more efficiently 5 — 7. Unlike these earlier studies that used a training set of drugs to develop a classification scheme, here we use dimensionality reduction to develop an unsupervised method to compare drug response profiles. Dimensionality reduction is used to reduce high-dimensional multiparametric data into a few informative dimensions. Reducing the number of dimensions also improves the visualizations of the spread of data. Classification, which is a supervised pattern recognition method, classifies a test input into a pre-defined set of output classes. In previous studies, drug similarities were identified by using drugs with known MOAs training set as classes and finding the probability of a test drug falling into each of these classes 6 , 7. These classification methods require a larger number of samples per drug for training an accurate classification model and do not provide visualizations of the spread of drugs. Furthermore, unique characteristics in the response profile of a test drug, which did not exist in the training set of drugs, will not be captured in the model. In contrast, dimensionality reduction methods, such as multidimensional scaling, consider differences between all drugs and create a low-dimensional space that optimally retains these differences. Dimensionality reduction methods are therefore used widely in analysing biological data 21 , 22 , For unknown drugs that are considerably different from the know drugs that it is being compared to ex: conolidine , this spatial spread indicates how different it is from the other drugs it is being compared to. Visualising drug placement also assists in identifying any additional drugs to add to the comparison. To our knowledge dimensionality reduction has only been applied twice for comparing network profiles in MEA data 4 , In the present study, we extend this use of dimensionality reduction by objectively quantifying the response profiles of a range of compounds with known actions to identify a molecular target for conolidine and cannabidiol. The failure of target panel-based screening to identify the MOA of conolidine 9 highlights the potential advantages of approaches based on neuronal networks for target identification. Such approaches would not only assist in the identification of mechanisms or efficacy but could also provide a platform for compound safety and liability assessment. Using PCA to reduce correlations between network parameters is a key feature of our workflow. When considering a multitude of parameters, it is inevitable that some of these parameters are correlated and giving the same importance weight to such correlated parameters diminishes the impact that other parameters have on the end result. An example of this is the multivariate analysis of variance MANOVA test that does not account for parameter correlation and cannot accurately determine similar drug responses Supplementary Table 3. Other studies use feature selection methods to reduce the number of parameters 3 , 6 , with insufficient detail about the actual process. All feature selection methods do not necessarily select features parameters that have minimum correlations. Most methods would select features that best separates the training set of drugs. This would usually mean that the selected features consists of correlations and that these features would not capture unique characteristics of a new drug that has a completely different profile than those in the training set of drugs. Therefore, we use PCA instead of feature selection methods to reduce the dimensionality of our data. Many studies use cortical cultures at maturity which produce rhythmic synchronised bursting 6 , 7. However, using cultures at 14—21 DIV produced network activity that included both synchronised bursting as well as spiking in the intermediary periods, which provides greater variety in activity. Therefore, the changes captured when a drug is applied also have greater variety. Therefore, we believe that our selection of tissue type also contributes to the effectiveness of the workflow. In our workflow, calculating average response profiles for each drug prior to dimensionality reduction also alleviates the problem of having small and unequal number of samples per drug, which most small-scale drug studies might face. Averaging all samples provides a more accurate representation of a drug, whereas individual samples may include considerable variations in the case of some drugs. Our workflow is centred on building a database of network response profiles of compounds with known MOAs and comparing these with the response profiles of novel compounds with unknown action. Despite the small size of the drug database deployed, we were able to identify and experimentally confirm a target of conolidine and cannabidiol. This was aided by the careful selection of compounds to be included in the database that spanned a relatively wide range of pharmacological MOA. Developing a more comprehensive database of experimental compounds and approved drugs will significantly improve the utility of the proposed workflow. There are several advantages to this approach. The database would only need to be generated once and it would naturally grow as it is utilised, 2. Cultured neuronal networks can readily scale to the analysis of thousands of compounds, 3. A larger database would permit the application of a broader array of pattern recognition methods. In vitro analysis revealed that conolidine and cannabidiol significantly blocked Ca v 2. In the peripheral and central nervous system, Ca v 2. Compounds that affect presynaptic Ca v 2. Pathologically, dorsal horn Ca v 2. Collectively, these data implicate Cav2. Our results suggest that conolidine and cannabidiol are likely to have other biological targets. Interestingly, cannabidiol is known to inhibit T-type channels T-type and Ca v 2. Therefore, the inhibition of network bursting observed here could be caused by both Ca v 2. Morphine also had a small Euclidean distance to cannabidiol and conolidine, and this may be a reflection of the fact that Ca v 2. This highlights that our approach may at times identify biological pathways implicated in the MOA rather than the target itself. It is also probable that conolidine has multiple targets, similar to cannabidiol and other alkaloids with complex MOAs. The development of human stem cell neuronal cultures used in conjunction with MEA technology promises to provide exciting new models on which to test compounds. The authors would like to thank Alexandra Delager and Tianran Wu for carrying out precursory experiments. We further thank Prof. We would also like to acknowledge UTT Pharma for the provision of cannabidiol. Pachernegg and G. The data that support the findings of this study are available from the corresponding author upon reasonable request. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. Sci Rep. Find articles by G D C Mendis. Find articles by G Berecki. Find articles by E Morrisroe. Find articles by S Pachernegg. Find articles by M Li. Find articles by M Varney. Find articles by P B Osborne. Find articles by C A Reid. Find articles by S Halgamuge. Find articles by S Petrou. Received Aug 7; Accepted Nov 15; Collection date Molecular target s , solvents and concentrations of drugs that were applied. Open in a new tab. Supplementary Material 3. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. GABA A receptor antagonist Metabotropic glutamate mGlu receptor antagonist HCN channel blocker

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