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ABSTRACT

TO, KIMBERLY THOAIPHUONG. Predictive Modeling for Developmental Toxicity of Engineered Nanomaterials. (Under the direction of Dr. David Reif).

Nanomaterials are defined as particles with at least one dimension on the nanoscale (1-100nm).

Although their toxicity is not yet fully understood, nanomaterials present themselves in a wide

variety of consumer products, from sunscreens to laser printers. Nanomaterials have been

proposed as drug delivery systems and as improvements to in vivo molecular imaging

techniques. Despite the promising applications of nanomaterials, understanding of their health

impact remains elusive. Further, because nanomaterials are already present in consumer

products, there is a sense of urgency in understanding nanomaterial toxicity as a preventative

measure. Alternative testing strategies, such as quantitative predictive modeling can be

developed to accelerate nanomaterial risk assessment. The development of a predictive model for

engineered nanomaterials is difficult due to inconsistencies in nanomaterial characterization and

the selection of endpoints within existing data. The models that have been tested use a

combination of measured physicochemical characteristics and computationally derived

descriptors. Informed design of these models can identify the characteristics that underlie the

molecular mechanisms of nanomaterial toxicity. Because of the difficulties in data aggregation,

imputation of characteristics and multi-source endpoint summarization would provide a full-rank

matrix for which predictive modeling can be performed. Of course, the quality of data imputation

is dependent on an abundance of high quality data, a known drawback of existing nanomaterial

toxicity data. By identifying which characteristics are most influential to nanomaterial toxic

behavior, we can decrease the minimum information required in datasets for data curation.

Chapter 1 is a review of available nanotoxicity data and predictive modeling efforts through

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imputation methods for high throughput toxicological data and subsequent hazard ranking.

Chapter 3 maps nanomaterial physicochemical features to multiple-endpoint developmental

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© Copyright 2019 by Kimberly T. To

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Predictive Modeling for Developmental Toxicity of Engineered Nanomaterials

by

Kimberly Thoaiphuong To

A dissertation submitted to the Graduate Faculty of North Carolina State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Bioinformatics

Raleigh, North Carolina 2019

APPROVED BY:

_______________________________ _______________________________ David Reif James Bonner

Committee Chair

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DEDICATION

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BIOGRAPHY

Kimberly T. To was born and raised in Raleigh, NC where she graduated from Wakefield High

School in 2010. She received her Bachelors of Science in Statistics from North Carolina State

University in 2014. Afterwards, she was coerced into attending graduate school by her

undergraduate mentor. She then began her doctoral studies in the Bioinformatics Program at NC

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ACKNOWLEDGMENTS

I would first like to acknowledge my advisor Dr. David Reif for his guidance and support. Thank

you for encouraging me to never stop never stopping. I would like to thank the Bioinformatics

program director, Dr. Spencer Muse for always believing in me and pushing me when I was too

afraid to push myself. I would also like to thank my committee members Dr. Jamie Bonner, Dr.

Denis Fourches, and Dr. Brian Reich for their contributions to my research. Thank you to Dr.

Robert Tanguay and Dr. Lisa Truong at Oregon State University for making this work possible. I

would also like to thank members of the Reif Lab: Dr. Skylar Marvel, Dr. Guozhu “Dale” Zhang,

Dr. Michele Balik-Meisner, Dr. Kyle Roell, Marissa Kosnik, and Dylan Wallis.

Thank you to my parents for always being there. I would also like to thank my sister and

brother-in-law for their constant love and support. To the friends I’ve made along the way, whose

commiseration made graduate school all the more better: Desiree Unselt, Saddy Wisotsky, Kevin

Gillespie, Jeremy Ash, and Alex Eyre. Thank you to my second family Juni Cuevas and Chase

Benson, for always happily telling me what I don’t want to hear. Thank you to my soul sister,

Ravid Gur, who never hesitated to take me to the Hall of Gratuitous Praise. Finally, I would like

to thank my best friend and partner, JD Pittman, who has been my life support throughout this

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TABLE OF CONTENTS

LIST OF TABLES ... vii

LIST OF FIGURES ... viii

CHAPTER 1: A Review of Predictive Modeling of Engineered Nanomaterial Toxicity ... 1

1.1. Introduction ... 2

1.2. Risk Prediction in Modern Toxicology ... 4

1.3. Nanotoxicology Data ... 6

1.3.1. Data Quality ... 6

1.3.2. Existing Sources ... 10

1.3.3. Nanomaterial Data Curation Initiative ... 13

1.4. Predictive Modeling ... 13

1.4.1. Grouping ... 16

1.4.2. Read-Across ... 19

1.4.3. NanoQSARs ... 22

1.4.3.1. Linear Regression Methods ... 22

1.4.3.2. Support Vector Machines ... 25

1.4.3.3. Neural Networks ... 26

1.4.3.4. Decision Trees ... 30

1.4.3.5. Comparison of Methods ... 31

1.5. Conclusion ... 31

1.6. References ... 33

CHAPTER 2: Characterizing the Effects of Missing Data and Evaluating Imputation Methods for Chemical Prioritization Applications using ToxPi ... 45

2.1. Background ... 46

2.2. Methods... 49

2.2.1. Data ... 49

2.2.2. Data Simulation ... 50

2.2.3. Data Imputation ... 50

2.2.4. ToxPi Calculation ... 51

2.2.5. ToxPi Score and Rank Evaluation ... 52

2.2.6. Statistical Software ... 54

2.3. Results ... 54

2.3.1. Missing Data ... 54

2.3.2. Wilcoxon signed-rank tests show most imputation methods result in significant changes to score ... 54

2.3.3. Minimum value imputation serves as a baseline for comparison ... 54

2.3.4. kNN causes unstable shifts in chemical ranks ... 57

2.3.5. Maximum value imputation causes uniform shifts in score and no shift in rank ... 57

2.3.6. Mean, binomial, and LLS imputation show similar effects ... 57

2.3.7. Variance of scores is stable in all methods except SVD ... 60

2.3.8. Overall ToxPi score variance differs significantly ... 60

2.4. Discussion ... 60

2.5. Conclusion ... 63

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CHAPTER 3: Multivariate Modeling of Engineered Nanomaterial Features Associated

with Developmental Toxicity ... 66

3.1. Introduction ... 67

3.2. Materials and Methods ... 69

3.2.1. ENM Characterization ... 69

3.2.2. Experimental Design ... 70

3.2.2.1. ENM Exposure Solutions ... 70

3.2.2.2. Zebrafish ... 70

3.2.3. Modeling ... 71

3.3. Results ... 72

3.3.1. Physicochemical Characteristic Analysis ... 72

3.3.2. The full model identifies key physicochemical characteristics ... 75

3.3.3. Reduced bagged decision tree shows similar performance to full model ... 77

3.3.4. Underprediction of more toxic ENMs improves with the inclusion of PCC ... 79

3.3.5. Variable wAggE responses across PCC reveal intricate characteristic interactions for toxicity response ... 79

3.4. Discussion ... 81

3.5. Conclusion ... 84

3.6. References ... 85

CHAPTER 4: Review, Current Work, and Future Directions ... 90

4.1. Introduction ... 90

4.2. On the Development of Predictive Models for Engineered Nanomaterial Toxicity ... 91

4.2.1. Quantitative Predictive Models for Nanotoxicity ... 91

4.2.2. Differences and Similarities in Nanotoxicity models ... 93

4.3. Implications of Data Imputation on Toxicity Profiling ... 93

4.3.1. Methods resulting in similar hazard score changes have different effects on mean rank change ... 93

4.3.2. Application to Nanotoxicity Data ... 95

4.3.3. Imputation Options for ENM Characteristics ... 96

4.4. Mapping Redundant Features to Toxicity Endpoint ... 97

4.4.1. Bagged decision tree identifies important variables and redundancy in feature space ... 97 95 4.4.2. Principal components analysis shows inherent groupings of PCC ... 97

4.4.3. Grid predictions show wAggE sensitivity to extreme PCC values ... 99

4.5. Current Work and Future Directions ... 101

4.5.1. Mapping Diameter, Circularity, and PDI to Behavioral Outcomes ... 101

4.5.2. Improving Published Experimental data for Predictive Modeling ... 103

4.5.3. Comparative Analysis of Imputation Methods for PCC ... 103

4.5.4. Comparative Analysis of Predictive Modeling Methods ... 104

4.6. Conclusions ... 104

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LIST OF TABLES

Table 1.1 Summary of Nanomaterial Registry records for biological interactions, adapted from nanomaterialregistry.org ... 11

Table 1.2 Summary of eNanoMapper records for in vitro toxicity data ... 12

Table 2.1 Values for the range of RMSE and range of Rank Change for each of 7 imputation methods, separated by the number of assays per slice ... 59

Table 3.1 The fourteen engineered nanomaterials tested ... 69

Table 3.2 Numeric distributions for each physicochemical characteristic. The minimum, mean (average), and maximum values for the ENM set are presented ... 73

Table 3.3 Variable Importance. Variables are sorted by calculated variable importance. Percent increase in MSE measures how model accuracy decreases when a single variable’s values are permuted ... 76

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LIST OF FIGURES

Figure 2.1 Conceptual overview of the simulation process and experimental design. Assays were randomly sampled from the original data based on a desired number of assays and assay sources (slices) so that the simulated datasets contained a subset of assays with arbitrarily assigned sources and all of 426 chemicals present in the original dataset. Simulated datasets were imputed and ToxPi profiles were calculated, with an overall summed ToxPi score given for analysis .. 55

Figure 2.2 Comparison of Imputation Methods Using ToxPi priority ranks. Mean ToxPi Rank Change between Imputed Simulated Data and Imputed Raw Data. Rank change was calculated by using the magnitude of difference between individual chemical ranks in the imputed raw data and the chemical ranks from each simulated dataset. Binomial, LLS, Maximum, and Mean show small magnitudes of change in rank. kNN shows a wider variation in rank change and therefore represents less stability in the method. Minimum value imputation and SVD present wider ranges in rank change, although the magnitude of change is smaller than kNN ... 56

Figure 2.3 Comparison of Imputation Methods by Toxpi Score. (a) Root Mean Square Error between Imputed Simulated Data ToxPi Scores and Imputed Raw Data Chemical Scores. After imputation and ToxPi calculation, scores were compared to the ToxPi scores using the standard “0” method. RMSE density distributions are separated by imputation method. The distribution of kNN is centered at the lowest RMSE compared to the other methods. Binomial, LLS, and Mean imputation are heavily overlapped. SVD is centered similarly, but shows a wider spread. Maximum imputation has the largest RMSE. (b) ToxPi Score Variance of Imputed Simulated Data ToxPi Scores. Amongst 1000 replicate simulations, the variance for each of 426 chemicals was calculated. Compared to SVD, all other methods present relatively low variability from chemical to chemical. SVD has an extremely wide range of ToxPi Score variability ... 58

Figure 3.1 Correlation plot of physicochemical characteristics (PCC). Plots along the diagonal show the distributions for each characteristic. Correlations are shown in the lower triangle, with colors corresponding to the direction and magnitude of the correlation ... 74

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Figure 3.3 Over and underprediction by ENM for each model. For each model (column) and ENM (row), the predicted wAggE is measured along the vertical axis and the observed wAggE is measured on the x-axis. Reported MSE are calculated as departures in the predicted wAggE from the observed wAggE. Black points indicate ENMs with underpredicted wAggE and blue points indicate ENMs with overpredicted wAggE. The diagonal line separates under and over prediction and the dotted line is at the χ20.5 = 2.37 threshold ... 78 Figure 3.4 Weighted Aggregate Entropy (wAggE) Measurements for ENM across selected

PCC. The left plot shows wAggE values for each ENM across the concentration curve. Darker shades of blue indicate higher wAggE values. The right plot shows PCC measurements for each ENM. Yellow shades are scaled for each descriptor so that darker shades indicate higher values of any one descriptor and lighter shades indicate lower values ... 82

Figure 4.1 Multiple linear regression diagnostic plot. Horizontal axis shows fitted values, vertical axis shows residuals. Horizontal line indicates residuals = 0 and the red line is a smoothed trend line ... 92

Figure 4.2 Mean chemical hazard ranking changes. The horizontal axis shows the number of assays per slice and the vertical axis measures mean rank change. Plots are split by imputation method and number of slices ... 94

Figure 4.3 RMSE between ToxPi scores from each imputed dataset and true scores. The horizontal axis shows the number of assays per slice and the vertical axis measures RMSE. Plots are split by imputation method and number of slices ... 94

Figure 4.4 Scree plot from principal components analysis with 11 PCC. The horizontal axis shows each principal component. The vertical axis describes the proportion of variance explained by the principal components ... 98

Figure 4.5 Biplot between the first two principal components. The horizontal and vertical axes plot the first and second principal components, respectively. PCC loadings are mapped with lines and colored by contribution to the principal components. ENM scores are plotted and colored by wAggE values ... 98

Figure 4.6 Concentration vs. diameter prediction plot. The left shows a surface plot of predictions across a grid of possible values. The plot on the right shows an overhead view, where the horizontal axis shows concentration and they vertical axis shows diameter and is colored by wAggE values ... 100

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Figure 4.8 Concentration vs. PDI prediction plot. The left shows a surface plot of predictions across a grid of possible values. The plot on the right shows an overhead view, where the horizontal axis shows concentration and they vertical axis shows PDI and is colored by wAggE values ... 100

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CHAPTER 1

A Review of Predictive Modeling of Engineered Nanomaterial Toxicity

Chapter 1 is a review of available nanotoxicity data and predictive modeling efforts through

grouping, read across, and nano-QSAR. Chapter 2 describes a simulation study assessing data

imputation methods for high throughput toxicological data and subsequent hazard ranking.

Chapter 3 maps nanomaterial physicochemical features to multiple-endpoint developmental

toxicity. Chapter 4 reviews the previous work and discusses future directions.

ABSTRACT

Engineered nanomaterials are man-made materials measuring with at least one dimension on the

nanoscale (1-100nm). Although their toxicity is not yet fully understood, nanomaterials present

themselves in a wide variety of consumer products, such as sunscreens or laser printers.

Nanomaterials have been proposed as drug delivery systems and as improvements to in vivo

molecular imaging techniques. Despite the promising applications of nanomaterials, their health

impact remains elusive. Further, because nanomaterials are already present in consumer

products, there is a sense of urgency in understanding nanomaterial toxicity as a preventative

measure. As such, the development of a predictive model for nanotoxicolgy will expedite

regulatory implementation for the safety of human and environmental health to nanomaterial

exposure. In this review, we discuss the limitations of existing nano toxicity data for the

purposes of predictive models. Implementations of grouping, read-across, and quantitative

nano-structure activity relationships (QNARs) for predicting toxicity are described. Reviewed QNAR

methods showed adequate prediction accuracy, but they used varying data sources with differing

experimental. For the purpose of developing a global predictive model, more uniform data are

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Keywords: Nanomaterials, Nanotoxicity, Predictive Modeling, Hazard Assessment, Risk

prediction, Read-Across, QNAR

1.1. Introduction

Nanotechnology is a growing field dealing with materials on the nanoscale, whose size lends

them unique characteristics that support a high range of potential applications [1–3]. For

example, nanomaterials that are generally considered nontoxic are present in consumer products,

such as cosmetics and apparel [4]. Further, nanomedicine is a subfield dealing with applications

of nanotechnology in medicine, with growing interest in drug delivery systems owing to certain

nanomaterials’ having the ability to cross membrane barriers for more targeted drug therapy [5,

6]. Various studies have shown that the small size creates an advantage of greater surface area

exposure at equivalent concentrations to larger materials [7, 8]. Besides the small size, studies of

the effects caused by nanomaterial structural characteristics or in vivo molecular interactions show different behaviors from their well characterized bulk counterparts [9, 10]. Thus, further

toxicity screening and hazard risk assessment are needed for both novel and existing materials.

Recognition of toxicity concerns has stimulated the developing field of nanotoxicology. A major

challenge has been the standardization of definition, methodology, and data [11]. For several

years, the term “nanomaterial” has had no universally acknowledged definition, with different

regulatory agencies providing varying, albeit congruous, descriptions of what constitutes a

nanomaterial [12]. However, a formal and discrete definition is deemed necessary for future

research as well as consistent regulatory purposes. In 2011, the European Commission released a

recommendation for the definition, with guidelines on determining how to classify an item as a

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size distribution for each unique nanomaterial, which, by design, will account for varying sizes

and aggregation or agglomeration that is common amongst nanomaterials. A flexible threshold

determines that a material will be classified as a nanomterial if at least 50% of the number size

distribution falls between the range of 1nm and 100nm [13]. The flexibility of this criterion

allows for future changes to threshold, should new information arise regarding size influences of

toxicity for a given nanomaterial.

Given this definition of the term “nanomaterial” one can consider the probabilistic nature of

nanomaterial measurements and descriptors as they relate to nanotoxicity. Common statistical

methods that have been explored use nanomaterial characteristics to categorize nanomaterials

with similar profiles and therefore potentially predict effects [14]. Amongst grouping and

categorization methods, nano-QSARs take advantage of an existing regression and

categorization framework that predicts biological outcome using material structural descriptors

as predictors. The overarching goal of these methods is to characterize nanomaterials so that

initial studies can benefit from accurate assumptions of toxic effects based on descriptors of

particular groups [15]. Grouping nanomaterials based on similar, yet naturally variable

characteristics underlies an intrinsically probabilistic view on predictive parameters. The

inconsistencies present among case-by-case chemical testing and screening studies highlight the

need for machine learning techniques that would effectively extract the maximum amount of

information from the available data and utilize this new information for newer substances with

similar parameter distributions. A handful of studies exist that show the accuracy of developed

networks for predicting toxicity and ranking chemicals for further testing. Implementation of

machine learning techniques for improvements to nano-QSAR predictions have been reported

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Grouping and read across methods represent a preliminary step to the process of toxicity

prediction, where the primary theory is that similarly characterized materials will behave in a

similar manner in regards to toxic behavior. However, the addition of quantitative components

can allow for more precise predictions, both within groups and in a global dataset. In fact, some

described predictive models utilize data from various literature sources, with a generalized

toxicity endpoint accurately predicted for a variety of nanomaterials. [19, 20]. The success of

these types of models depends on common descriptors being present in each individual dataset.

Unfortunately, data aggregation can result in mismatched data, for which different descriptors

have been described and therefore hinders the development of a complex mathematical

relationship between descriptors and endpoint. Of course, data imputation can resolve these

issues by filling in gaps using a predictive process [21].

In this paper, we review the progress of predictive nanotoxicology and the methods that have

been implemented. We begin with an overview of traditional methods in toxicology and the

implications of current data concerns in nanotoxicology. In the next sections, we cover grouping

and read across for nanomaterials and describe the abundance of statistical methods applied to

nanotoxicity data.

1.2. Risk Prediction in Modern Toxicology

Currently, toxicity testing of new and existing chemicals is performed using the OECD

guidelines for the Testing of Chemicals [22]. These guidelines list a variety of relevant toxicity

endpoints that take into account both human and environmental health impacts. Having a

standardized reporting protocol for toxicity testing is beneficial so that data from different

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in computational power and high throughput screening (HTS) limited toxicity testing to

case-by-case studies, making data aggregation and consequently chemical profiling quite tedious [23,

24]. Technological advancements have allowed for improved HTS methods for thousands of

chemicals, resulting in thousands of datasets that have been compiled and made publicly

available through the PubChem database [25].

Included in this database, the U.S. government consortia on Toxicity in the 21st Century (Tox21) and Toxicity Forecaster (ToxCast) have implemented in vitro HTS assays for quick and efficient toxicity testing of both well-studied chemicals and new or untested chemicals with an ultimate

goal of ranking and prioritizing chemicals to identify and further test those with highest toxicity

[26–28]. In vitro HTS is becoming more prevalent due to the growing number of insufficiently tested or untested chemicals and a greater emphasis on reducing animal testing, a recognized

goal of the EU's REACH program [29]. However, this type of testing typically results in large

amounts of data that need to be analyzed [24, 30]. As such, the developing a computational

method for predicting chemical toxicity is a necessary step, as both time and resources can be

saved by utilizing data at hand.

Moreover, calls for data collection and data gap filling in hazard assessment in the REACH

protocol are dependent on functional in silico methodology [31]. Typically, the developed methods, such as read-across and QSARs, take advantage of structural similarities amongst

materials to group them and predict chemical behaviors based on available data for tested

chemicals within the same groups [32]. The inclusion of molecular descriptors have been

recommended for better predictive power, as well as more complex methods such as neural nets

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molecular descriptors that can predict NOEL using an artificial neural network with data from

the Japan Existing Chemical Database [36].

The OECD has acknowledged the need to develop new testing strategies or harmonize existing

testing strategies for applicability to nanomaterial safety assessment. Because nanomaterials and

larger materials have been shown to differ in both size and biological behavior, the existing

methods may inadvertently miss significant behaviors of nanomaterials that typically would not

need to be considered in standard toxicology [37]. Nanotoxicology itself has adapted standard

QSARs for use with nanomaterials into nano-QSARs [38]. nQSARs or QNARs mirror the

methodology of standard QSARs while incorporating nano-specific descriptors such as

zeta-potential, surface descriptors, or molecular shape [39]. For example, Mu et. al used existing data

from 16 metal oxides to develop an improved nano-QSAR model that included two new

parameters: enthalpy of formation of a gaseous cation and polarization force [15].

Recommendations for continuing improvements to nano-QSARs suggest the inclusion of more

complex methods, such as Fjodorova's nano-QSAR built using artificial neural networks using

descriptors such as enthalpy of formation of a gaseous cation and those relating back to

placement on the periodic table [18]. It is pertinent that these differing characteristics be

considered for nanotoxicity predictions because nanomaterials have been shown to behave

differently than their bulk counterparts [40].

1.3. Nanotoxicology Data 1.3.1. Data Quality

As the field of nanotoxicology grows, the amount of data will continue to increase. However, we

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acquiring data can be completed by (1) finding it in an existing repository (surveyed below), (2)

downloading the submitted data file from the corresponding publication, or (3) extracting the

data from the corresponding publication, when no explicit data submission exists. The methods

for acquiring data are fairly straightforward; extraction from a publication can be tedious, but

software have been developed for these purposes [41].

Once data are acquired, we need to assess data quality. Ideally, we would like for the data to be

within a repository and directly comparable to other datasets in the repository. Because

nanotoxicology is new and quickly developing, the research interests within the field are quite

diverse. Depending on specific research interests, different datasets may report different

physicochemical characteristics (PCC), making data comparability more difficult. This can be

prevented by the development of a globally accepted minimum information standard, similar to

those developed in other fields. Although this has not yet been established in nanotoxicology, the

Nanomaterial Registry (RTI) has developed their own Minimum Information about

Nanomaterials (MIAN) [42] that contains guidelines for reporting PCC, along with the methods

used to measure them. This MIAN is applied within the database itself, allowing for more

detailed filtering as well as more streamlined identification of similar nanomaterials [43].

However, if the goal is to use currently available data for risk prediction, an alternative solution

is with the use of Nano-Quantiative Structure-Property Relationships for predicting nanomaterial

properties. Nano-QSPRs have been successfully developed for zeta potential [44, 45], and could

therefore present a preliminary data processing step for predicting properties known to affect

toxicity, but may be difficult to measure or not available in a given dataset.

Another issue affecting data quality is the lack of a standardized naming convention. For

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acquisition. By developing a standard naming convention, these different names can be linked

together, and data can be extracted more efficiently [46]. The Nanoparticle Ontology (NPO) was

developed for the purposes of sharing diverse data for nanomaterials in cancer research [47]. The

structured ontology allows for simple descriptions of complex structures, as well as the methods

used. Although NPO was developed for cancer research, it is designed for growth and has been

adapted for use in nanomaterial annotation. In this implementation, the NPO is converted to a

string expression that can be computationally processed for assessing similarities and differences

between nanomaterial structures [48]. For the purposes of data aggregation, the NPO has been

used to collect nanoparticle properties from cancer treatment literature with high precision [49].

In addition, the NPO has been adapted for use in the eNanoMapper database, discussed below.

A common data file format is also necessary for successful data curation because it allows for

dataset comparability. ISA-TAB-Nano is a file format using NPO, developed by the NCIP Nano

WG [50]. ISA-TAB-Nano is an extension of the existing file format ISA-TAB with adaptations

for nano-specific descriptors. ISA-TAB-Nano uses 4 labeled spreadsheets that together describe

reference information, study protocols, sample endpoint measurements, and descriptions of the

material. The information provided and ease of use makes ISA-TAB-Nano an important tool in

the goal of nanotoxicology data curation. ISA-TAB's predecessor MAGE-TAB included its own

set of minimum information standards for microarrays [51], so the use of standardized language

and an implementation of minimum information standards would make ISA-TAB-Nano the gold

standard for nanotoxicology data keeping.

The issues discussed thus far pertain to data keeping, with a seemingly simple solution of

standardization that can be easily adopted by researchers of nanosciences. A more difficult issue

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in-house measurements [52], and therefore need to be validated. Sources of variation in these

descriptions are not limited to manufacturer error, but can arise from transport and handling [53].

Further, the Nanotechnology Characterization Laboratory (NCL) has published a list of common

pitfalls uncovered in their efforts to characterize nanoparticles [54]. A common theme is the

mis-attribution of toxic potential to nanoparticles in a sample, either due to contamination or

nanomaterial composition [55]. This has become increasingly problematic as evidence for

interactive and even synergistic effects of nanomaterials and co-existing contaminants has arisen

[56]. For example, a co-exposure of silver nanoparticles with cadmium have been shown to

decrease cell viability in HepG2 cells when compared to silver nanoparticles or cadmium as the

sole exposure [57]. Similarly, increased bioaccumulation of various heavy metals was shown in

C. Elegans after co-exposure with titanium dioxide nanoparticles [58]. At first glance, a solution to this issue is to test every sample in house for contamination and to perform experiments on

more complex nanoparticle formulations to ensure that an ingredient of any nanoformulation is

not the main driver of response. However, this can be a significant cost burden to individual labs.

A proposed alternative is to estimate probability distributions for nanoparticle characteristics to

allow for estimates of true particle characteristics [59]. Because we are dealing with disparate

datasets, statistical methods will be an important tool for imputing the data gaps inherent to data

curation. At present, the development of a robust predictive model for nanotoxicity is limited by

the amount of available high quality data. Imputing nanomaterial physicochemical characteristics

with manufacturer labels has been shown to accurately assess toxicity when used with a

physicochemical characteristic weighting scheme [60]. However, the known discrepancy

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development using statistical imputation methods may improve the data gap issue while

providing the most accurate estimate of the missing physicochemical characteristic.

1.3.2. Existing Data Sources

Despite the known data quality challenges, there have been several efforts to aggregate

nano-related data. The Nanotechnology Consumer Products Inventory lists 1814 consumer products

containing nanomaterials and categorizes them based on verification that the product contains

nanomaterials [61, 62]. However, it does not contain scientific data that would be useful for risk

assessment. Similarly, the Nanowerk Nanomaterial Database lists nanomaterial suppliers for

4000 different nanomaterials along with the manufacturer product descriptions [63].

The DaNa 2.0 Knowledgebase was developed by an interdisciplinary team of researchers

providing input on 26 market-relevant nanomaterials and their effects on humans [64]. The

knowledgebase allows the user to search by application or by material and provides bountiful

information on potential exposure routes and behavior in both humans and the environment. The

team developed a “Literature Criteria Checklist” that assesses the quality of a literature source

for use in the knowledgebase. With a wealth of scientifically-based qualitative data, DaNa2.0 is

targeted to both non-scientific users, such as consumers or stakeholders, and to scientific users.

However, the availability of quantitative data is minimal, limiting the application of DaNa2.0 for

predictive modeling.

For quantitative toxicity data, two sources of nanomaterial data are the Nanomaterial Registry

and eNanoMapper. The Nanomaterial Registry was created in 2013 through a partnership with

the National Institute of Environmental Health Sciences (NIEHS), National Institute of

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2014, the registry contains 2031 records for engineered nanomaterials, with biological data from

608 different studies. Data are sourced from databases, data partnerships, and manufacturer data

sheets (Table 1). Before entry into the registry, data are scrutinized for adherence to the

registry’s Minimum Information about Nanomaterials (MIAN) standards. The MIAN includes an

“Instance of Characterization” that indicates when the nanomaterial was characterized, which

can alleviate discrepencies in characteristic measurements.

Table 1. Summary of Nanomaterial Registry records for biological interactions, adapted from

nanomaterialregistry.org.

Data Source Host

Organization

Type Focus Source

Records Registry Record Ref Cancer Nanotechnology Laboratory (caNanoLab) National Cancer Institute (NCI)

Database Cancer research using

nanomaterials

2336 in vitro assays; 100 in vivo assays 348 unique nanomaterials from 266 studies [123, 124] Nanomaterial-Biological Interactions Knowledgebase (NBI) Oregon State University Data Partner (raw data) High throughput studies on nanomaterial toxicology using an embryonic zebra fish model 165 records for 31 unique nano cores

82 records for 22 unique cores

[125] NanoComposix, Inc. Data Partner (Product specification sheets and publications) Manufactured metal nanoparticles and the studies using them 35 unique nanomaterials [126, 127]

The eNanoMapper prototype database was published in 2015 from the European Commission’s

Seventh Framework Programme (FP7) with a distinct goal of facilitating the management of

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literature source describing the protein corona formed due to nanoparticle-biological interactions

[67, 68]. Since its initial release, the eNanoMapper prototype database has grown to include

user-submitted data from European Commission funded projects. Among these data sources, 3

include in vitro toxicity measurements (Table 2).

Table 2. Summary of eNanoMapper records for in vitro toxicity data.

Nanomaterial Contributor Name

Materials with Toxicity Data

Focus Ref

FP7 MARINA 6 Substances Develop and validate the Risk Management Methods for Nanomaterials

[128]

MODENA 41 Substances Develop Quantiative Nanostructure Toxicity Relationship tools [129]

Protein Corona Paper

121 Substances Characterize protein corona and effect on net cell association [68]

In addition to this comprehensive database, the eNanoMapper project has released Jaqpot

Quattro, a web-based platform for generating nano-QSARs and read across models using data

provided by the eNanoMapper database [69]. Jaqpot supplies a number of algorithms for both

regression and classification with options for selecting predictor variables and an endpoint of

interest. Documentation for this utility is extensive, allowing for self-guided analysis of new or

existing nanotoxicity data.

The eNanoMapper project has developed its own ontology as an extension of the NanoParticle

Ontology (NPO), which gathers parts of existing ontologies to meet the overall ontological needs

for determining nanosafety from a wide array of sources. Further supporting this overarching

goal, the eNanoMapper database supports the input of different file formats and processes them

for inclusion in the database. So, through the developed ontology and ability to process a diverse

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1.3.3. The Nanomaterial Data Curation Initiative

The Nanomaterial Data Curation Initiative (NDCI) from the NCIP NanoWG has released

publications detailing the challenges in a large scale data curation effort. Recommendations are

provided for standardization of a minimal information checklist, vocabulary to be used, and

evaluation of data quality and completeness [70]. These needs are apparent due to the differing

foci from different stakeholders. For example, individual laboratory groups are interested in

molecular and cellular aspects, while other groups are more concerned with consumer products.

These differing foci have different data requirements and the groups interested in them would

therefore have different ideas of minimal information required and data completeness and

quality. The workflow described by the NDCI includes communication with authors in attempts

to fill data gaps, as well as the development of a model describing the relationships between

physicochemical characterizations [71, 72].

The development of this model has potential for resolving the different informational

requirements from different stakeholders and highlights the modeling needs at the foundational

level of nanotoxicology. High quality, complete characterization data would be useful in the

understanding of nanomaterial structure and the development of nano-QSARs, which can inform

on the toxic potential of nanomaterials and determine relationships between structure and

behavior that would allow for predictive modeling of nanotoxicology.

1.4. Predictive Modeling

Legislation from the European Union has promoted decreased usage of non-human vertebrate

animal models, in favor of developing alternative testing strategies for chemical risk assessment

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growing number of modifications altering their behavior, these alternative testing strategies can

improve time and money that would otherwise be spent on single case studies.

While high throughput screening (HTS) permits rapid testing of chemicals, the immense volume

of data generated introduces the need for new computational analysis methods to make sense of

the results [74–76]. Likewise, the use of high throughput methods for exploring nanotoxicity and

procuring data for the development of predictive models has been suggested due to coverage of a

wide variety of nanomaterials [77, 78]. Applications of high throughput screening methods for

nanomaterials have been described in various in vivo model organisms, such as the observation of hatching in zebrafish embryos [79] or behavioral alterations in C. elegans [80]. Similarly, in vitro methods have been described, with an emphasis on understanding molecular mechanisms for nanomaterial toxicity [81, 82]. Though the use of predictive models for these types of data

have been scarcely described, there is evidence for its success. For example, results from a

high-throughput screen using a zebrafish embryo toxicity test were used to develop a self-organizing

map that definitively separates materials and doses [83]. Therefore, we observe that these HTS

methods can preclude the development of a predictive model by heightened understanding of the

mechanism for nanomaterial toxicity.

A major potential use for these types of predictive models are for predicting the toxicity of

untested chemicals within a model’s applicability domain, commonly identified using leverage

values [84]. However, as previously mentioned, without a globally accepted minimum

information criteria for nanomaterials, the aggregation of data presents missing data for

measurements that may have predictive influence on nanomaterial toxicity [60]. It has been

shown in standard toxicological settings that this missingness can affect end-stage applications,

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aggregating this heterogeneous data is a two-stage experimental design. The first stage uses

multi-source experimental data and applies geospatial stochastic kriging to identify data gaps to

be mediated in the second stage. Alternatively, similarities in ENM properties may allow for

model-based imputation [86]. The recent application of Quantiative Structure-Property

Relationships to nanomaterials uses quasi-SMILES, the coupling of SMILES structural

descriptors and intrinsic size measurements, to predict size-dependent properties such as Young's

modulus, zeta potential, dispersion quality, and thermal conductivity [87]. In fact, using only

quasi-SMILES to derive descriptors for multiple linear regression, one group has developed a

nano-QSPR for zeta potential that boasts a validation R2 ranging from 0.67 to 0.82. Of course it is described within these papers, that nanomaterials with less deviating characteristics require

further improvements to this nano-QSPR [45]. In general, these types of models aim to improve

the development and performance of predictive nanotoxicology models by providing structurally

and mathematically based methods of data imputation.

In this section, we first discuss the grouping of nanomaterials based on similar characteristics

and the necessity of high quality grouping paradigms for successful prediction in read-across.

We then discuss the use of nanomaterial read-across and the inclusion of quantitative approaches

for improved prediction. Next, we cover nano-QSARs and the underlying statistical methods

quantifying the relationships between nanomaterial descriptors and toxicity endpoints. We begin

with an overview of multiple linear regression, a relatively simple modeling technique, and then

discuss more complex methods and the use of Bayesian inference to improve model performance

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1.4.1. Grouping

Grouping methods are used to categorize chemicals based on similar physicochemical

characteristics or toxicological profiles. Chemicals that are grouped together can then be

assumed to exhibit similar behaviors. Grouping facilitates the use of chemical read-across,

wherein the prediction of a chemical's toxicity is based on its group assignment. Therefore,

grouping methods have been recommended by the OECD and ECHA as a means to reduce

unnecessary testing by allowing for in silico toxicity screening [31, 88].

The ECHA has published its own recommendations for performing grouping and read-across for

nanomaterials. In this stepwise procedure, nanomaterials are characterized by their

physico-chemical characteristics and subsequently grouped by these attributes as well as known

behaviors and reactivity. The results from chemical read-across using these methods is then

assessed and used to improve grouping criteria [89]. A case study of 6 nanoform types of TiO2

implemented a simplified version of the ECHA grouping framework to group an aggregated

dataset consisting of nanomaterial attributes and comet assay results. The working hypothesis for

this study was that TiO2 surface coatings or surface impurities decrease genotoxicity of the

material. Hierarchical clustering and principal components analysis revealed two distinct groups,

genotoxic and non-genotoxic. The relevancy of surface coatings and surface impurities was

reaffirmed using random forests variable selection and predictions of genotoxicity based on these

characteristic-based groupings consistency with existing comet assay results [90]. A similar

grouping method has been applied using pulmonary inflammation data from nanomaterial

exposed rodents. In this study, hierarchical clustering was applied to the toxic endpoint, in this

case potency calculated from benchmark doses, to generate 4 potency groups. A predictive

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subsequently used for predicting the potency of newer materials [91]. This study differs from the

previously described TiO2 study in that it utilizes toxicity information as the grouping criteria,

highlighting the utility of non-structurally based descriptors for grouping and model

development.

Although grouping and read across based on structural similarities is quite standard, it is noted

that the poorly understood structure of nanomaterials requires use of extraneous attributes such

as mechanistic based descriptors or toxicological endpoints [89]. However, an early

implementation of nanomaterial grouping bypasses this notion of nano-specific mechanisms and

instead uses 3 general attributes that allude to mode of action. The first category consists of

chemicals exhibiting chemically mediated toxicity (i.e. release of metal ions); the second

category consists of fibrous nanomaterials; and the third category consists of respirable granular

and biopersistent, but not fibrous nanoparticles [92]. This grouping scheme has not been widely

utilized, however, the use of mode of action as a characteristic attribute underscores a growing

emphasis on the use of a broader perspective for defining grouping criteria.

Arts et.al reviewed grouping methods for nanomaterials and highlighted the need for

nanomaterial life-cycle based grouping, arguing that the proposed grouping methods focus on a

single point in the nanomaterial life-cycle, whereas an entire life-cycle based approach would

capture a more informed toxicological profile [93]. This proposed grouping mechanism was

subsequently implemented in the DF4nanoGrouping framework by the European Centre for

Ecotoxicology and Toxicology of Chemicals (ECETOC) Nano Task Force [14]. Nanomaterials

can be assigned to one of four groups: (1) soluble nanomaterials, (2) biopersistent high aspect

ratio nanomaterials (HAR NMs), (3) passive nanomaterials, and (4) active nanomaterials. These

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properties, Tier 2 assesses system-dependent properties, and Tier 3 confirms passive vs. active

nanomaterials using in vivo screening. The utility of DF4nanoGrouping was discussed in a case

study, which exemplifies the reduction of animal-testing needs by using different hazard

assessments for each group [94].

This robust framework has been coupled with specific testing methods and analytical procedures

that allow for life-cycle based grouping. In one such study, data from assays satisfying the Tier 3

initiative were collected and processed through classification trees using nanomaterial

descriptors. In this way, specific toxicological endpoints can be linked to the most relevant

descriptors for which between-group diversity can be detected [95]. From this study, we observe

the applicability of the DF4 framework to existing data for grouping nanomaterials as passive or

active. This tiered methodology can also be implemented outside of the confines of the

predetermined grouping. In a 2018 study by Scott-Fordsmand et.al, 8 different nanomaterials are

partial order ranked within the scope of each of the three tiers, as described by Bruggeman and

Patil [96]. The inclusion of a threshold in the chemical matrix allows for rank comparison of the

nanomaterials to the threshold rank. This naturally yields three groups: (1) Nanomaterials that

rank lower than the threshold, (2) Nanomaterials that rank higher than the threshold, and (3)

Nanomaterials that rank equally to the threshold [97]. Though this method can be useful for

determining relative toxicity amongst a subset of nanomaterials, it does not support a global

grouping framework, as group assignments are dependent on the selected threshold parameters

and the nanomaterials in question. An accurate grouping scheme is important for read across, as

it precedes the prediction of toxicity; that is, recognizing the nanomaterial parameters which

influence within-group variability and between-group variability will improve grouping and lead

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1.4.2. Read Across

Chemical read across is a successful non-testing strategy in toxicology that has been

recommended for filling data gaps in nanotoxicology data [89]. The general principle of read

across is that two physically or structurally similar chemicals should have similarly measured

activity for any given endpoint. As a result, we expect that the activity of one or more chemicals

can be predicted by the activity of one or more similar chemicals.

Successful read-across is dependent on the available chemical descriptors and an accurate

grouping mechanism, based on either these descriptors or known toxicity measurements, as

described above. The European Union FP7 MARINA Risk Assessment Strategy is broken into

two phases. The first phase focuses on problem framing, where relevant exposure scenarios are

considered for a target material. Data are then collected and the chemicals within this aggregated

data are grouped based on a determined grouping mechanism. So in this risk assessment strategy,

we see a high emphasis on adequate grouping and ample, high quality data. The second phase

focuses on risk assessment. Based on data collected in Phase 1, a hypothesis is created that

emphasizes similarities between the target material and relevant source material. Specific

endpoints can then be predicted for the target material, if enough data exists. Otherwise, further

testing is needed [98].

To date, the described nano-read-across approaches have been predominantly performed on sets

of metal oxide nanoparticle data, an important subgroup of nanomaterials that are prevalent in

consumer products [99]. Gajewicz et. al performed two case studies on data from metal oxide

exposure to E. Coli and to HaCaT cells. For E. Coli, grouping was performed on 10

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same oxidation state as that in the metal oxide structure (ΔHMe+). Cytotoxicity for both the

training set of 10 nanomaterials and the validation set of 7 nanomaterials was 100% accurate. For

the HaCaT cell line, 10 nanomaterials were grouped using a single descriptor, Mullikan's

electronegativty. The training set featured 80% accuracy, while the validation set of 8

nanomaterials featured accuracy of 87.5% [100]. From this example, we see the importance of

deriving an accurate grouping scheme prior to read-across, as it allows for data-supported

toxicity prediction for untested materials. It is also noted that the read-across framework is best

suited for a dataset of this size, compared to QSAR methods which require a larger number of

observations.

However, the ability to aggregate data from different sources would increase the amount of data

available for nanomaterial toxicity and allow for a more comprehensive grouping analysis and

therefore, more accurate read-across. A multi-nano-read-across approach has been described for

metal oxide nanoparticles. In this study, data was obtained from 15 studies, each using one of 9

different cell types under different testing environments. Grouping was performed using

nanoparticle structural descriptors and normalized toxicity measurements in a self-organizing

map. Overall, species-dependent mode of action and variable experimental conditions were

found to affect similarities between prediction results from different species, although

inter-species similarities were found, highlighting the promise for developing an accurate read across

model based on data from multiple species and studies [101].

Although the grouping mechanism plays a vital role in read-across performance, understanding

the mathematical relationships between nanomaterial descriptors in eliciting toxicity would

improve the predictive power of the read-across model. A criticism of read-across is the

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predictions [102]. Therefore, a quantitative predictive approach would alleviate this bias by

relying on numerical relationships for determining similarities between materials, improving the

reproducibility of nanomaterial read-across results. The Quantiative Read-Across Approach for

Nanomaterials (Nano-QRA) uses an algorithmic approach for filling in missing data about a

chemical’s toxicity endpoint by identifying linear trends within its assigned group based on one

to three nanomaterial descriptors. In this preliminary study, the accuracy of a quantitative

approach was demonstrated using a limited set of descriptors with the previously described metal

oxide nanoparticle dataset [103]. Although the authors note similarities in methodology between

Nano-QRA and kNN-QSAR, Nano-QRA is considered more appropriate for scarce datasets.

A streamlined framework for quantitative nano-read across was developed using the

protein-corona dataset [104]. This framework, nano-Lazar, is an extension of an existing read-across

framework, Lazar [105]. To explore extension options, three similarity protocols for grouping

were tested based on structural similarity, property similarity, or biological similarity. After

grouping, three local regression algorithms were applied to each group to model the toxicity end

point: net cell association. Results from this study indicate more accurate predictive outcomes

when using measured nanoparticle descriptors with the random forests model. Because

Nano-Lazar performs grouping and local prediction, it can be described as both a read-across model

and nano-QSAR model. In fact, Nano-Lazar was developed using a large, well-characterized

dataset, which is a known necessity for developing QSARs and nano-QSARs. So, although

read-across can give qualitative information about a nanomaterial’s potential toxicity, the inclusion of

quantitative prediction may alter the underlying advantages of qualitative read-across. We see

from these read-across examples that despite the advantages of read across, quantitative solutions

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1.4.3. Nano-QSARs

A common quantitative method for predicting nanotoxicity is the application of QSAR-like

models to nanomaterials. Various names have been given to describe this use of mathematical

modeling for predicting nanotoxicity, e.g. Nano Quantitative Structure-Activity Relationships

(nano-QSAR), Quantitative NanoStructure Activity Relationships (QNAR), or Quantitative

Structure-Toxicity Relationship (QSTR). A common practice in applying QSAR to

nanomaterials is to extend the descriptor space past structural descriptors to include inherent

nanomaterial features as well as experimental descriptors.

1.4.3.1. Linear Regression Methods

Perhaps the most simple nano-QSAR technique is to use linear regression, wherein a specified

nanotoxicity endpoint is modeled through a linear relationship to the selected descriptors. A

common practice is to use a descriptor selection technique that will pick descriptors most

relevant to the endpoint prior to model development. Use of a subset selection method decreases

computation time needed to perform an exhaustive search over all possible subsets of descriptors

to identify the globally optimized model. In one study, Puzyn et. al began with 12 calculated

structural descriptors for a set of 17 metal oxide nanomaterials, ranging in size from 15 to 90nm

[106]. The genetic algorithm was implemented, where each row of features for a particular

nanomaterial is regarded as a parental chromosome. Evolution of these chromosomes is then

modeled through subsequent generations which represent the fittest combination of features

selected [107]. In the Puzyn study, only a single descriptor, ΔHMe+, was selected for predicting

cell viability in E. Coli. The reported model R2 of 0.85 and cross-validation regression

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In one study, cytotoxicity data for these 17 metal oxide nanomaterials was used along with

descriptors derived from the material's molecular formula and data from the periodic table. In

this case, variable selection was performed using stepwise multiple linear regression, so that

inclusion of a particular descriptor is based on statistical significance in the linear regression

model [108] The final model included only a single descriptor, the charge of the metal cation

corresponding to a given oxide, χOX, with reported R2 and Q2 values of 0.84 and 0.81,

respectively. Interestingly, neither ΔHMe+ nor χOX are size-dependent descriptors, but rather are

mechanistic descriptors, informing on expected biochemical behavior of the materials in a given

medium. Of course, the final model is dependent on the subset selection method. In a subsequent

analysis, cytotoxicity measurements from Puzyn’s study and another metal oxide nanomaterial

study [109] were aggregated, forming a training dataset of 16 metal oxides and 26 calculated

chemical descriptors [15]. Variables were selected by first considering Pearson's correlation

coefficient, removing redundant variables to avoid autocorrelation. Clustering and principal

components analysis were performed to select four optimal descriptors. All potential models

derived from this set of four descriptors were considered, and a final model containing two

descriptors, polarization force (Z/r) and ΔHMe+, was selected based on the coefficient of

determination. R2 for the 16 metal oxides was 0.882, while Q2 for internal validation using various validation methods ranged from 0.846 and 0.910, indicating improved model

performance when including the additional Z/r parameter. It is therefore clear that the

performance of the model depends on the selection of accurate molecular descriptors specific to

a given endpoint.

An adapted version of multiple linear regression is k Nearest Neighbors (kNN) regression, where

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predicted using a subset of data points most closely resembling attributes for the endpoint in

question. Of course, performance of kNN regression is dependent on the selection of k, which

can be determined by cross-validation or some optimization procedure. Another important factor

in the performance of the resulting model is the distance metric, with Euclidean distance being

the common method used. In a study of 34 synthesized gold nanoparticles ranging in size from

5-10nm and with differing surface ligands, Euclidean distance was used to compare nanoparticle

profiles, comprised of 29 descriptors [110]. The value of k was selected using leave-one-out

cross-validation and a range of possible k values and selecting the k that maximizes Q2. Variable selection is performed in a similar manner, with an initial model built using randomly selected

variables, updated iteratively and selecting for the model with the highest Q2 [111]. Using this kNN strategy, 4 final QNAR models were developed for each of 4 endpoints: cellular uptake in

human lung cells (A549), cellular uptake in human kidney cells (HEK293), oxidative stress

(measured by HO-1 protein levels in A549 cells), and hydrophobicity (measured by partition

coefficient). Reported R2 for these models ranged from 0.990 to 0.995, while external validation Q2 values ranged from 0.768 to 0.930. As with the multiple linear regression nano-QSARs, the variables selected for each of these four models give insight into the mechanism for toxic effect.

For example, a common feature in models for cellular uptake were descriptors related to

hydrophobicity. Similarly, Fourches et.al modeled the cellular uptake of 109 superparamagnetic

nanoparticles with different surface modifiers in PaCa2 cells using k Nearest Neighbors

Regression [112]. The resulting internal validation R2 ranged from 0.65 to 0.80 and after removing materials outside of the applicability domain, 0.67 to 0.90. The external validation Q2 was 0.72 and after applying the applicability domain, 0.77. Among the descriptors chosen for

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variable selection in deriving a mechanism based toxicity prediction. By using the k nearest

neighbors for prediction, kNN regression is inherently based on the same principle as grouping

and read-across; that is, a material's toxicity can be predicted by the toxicity of similar materials.

1.4.3.2. Support Vector Machines

In addition to kNN regression, Fourches et. al implemented Support Vector Machine (SVM)

classification by converting bioactivity to a binary endpoint indicating biological activity.

External validation of the developed model yields 73% accuracy, with 60% specificity. SVM

classification and regression use a kernel function to non-linearly discretize endpoints. Parameter

selection for these kernel functions can be performed using a grid-search coupled with

cross-validation. In one such case, EC50 data from 17 metal oxide nanoparticles was obtained from

literature, with six derived molecular descriptors Using a radial basis function, a support vector

machine regression model resulted in training and test set R2 values of 0.903 and 0.916, respectively [113]. When compared with the results from stepwise multiple linear regression

performed on the same dataset, R2 values indicate high agreement between the two models (MLR Training and Test R2 = 0.877 and 0.848, respectively). From this example, we find that non-linear SVM benefits from marginally higher accuracy in prediction, however model

interpretability is higher for multiple linear regression, where feature weights can be directly

associated with endpoint variability. Regardless, variable selection can be performed for

non-linear SVM using cross-validation to search through models of different sizes and selected

variables can then be individually analyzed against the endpoint to understand impacts to toxicity

as has recently been done with Puzyn's metal oxide nanoparticle EC50 data [114]. In this case,

37 variables were derived using minimum redundancy feature selection to remove highly

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algorithm with support vector regression, a 3-variable model with the lowest RMSE identified

three relevant variables: atomic ratio of oxygen to metal (RAatom), enthalpy of melting ΔHm, and

cohesive energy (Ecoh). In the next step, a residual bootstrapping technique was implemented to

improve model predictions by refitting the model to endpoints updated with residual information.

Comparison of the standard support vector regression and the support vector regression with

residual boostrapping indicate lower R2 values for each test set using residual boostrapping, however this difference is not statistically significant and R2 values using the bootstrap method are less variable across test sets, indicating better model stability. We see from these results that

SVM regression is quite similar to the described linear methods above, wherein a dataset of

nanomaterial features can be mathematically modeled to the endpoint of interest. Using more

advanced methods for refining these models can result in robust predictive models for

nanotoxicity.

1.4.3.3. Neural Networks

When compared to the more complex neural network method, SVM shows varying performance,

dependent on the data to be analyzed and the linearity of the mathematical technique applied.

Neural networks represent another class of analytical methods derived from a simplified model

of neurological activity. The most basic neural network, the feedforward neural network, moves

in a single direction connecting an input layer, hidden layer, and output layer. Applied to QNAR,

the input layer will consist of nanomaterial descriptors that are fed to the hidden layer. The

hidden layer contains an activation function that transforms the descriptors using some

mathematical function. Each node within the hidden layer is then sent to the output layer via a

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between layers are weighted, with these weights being continuously updated based on prediction

errors to tune the model for accuracy.

A comparative study quantifying lipid membrane disruption in rat lung epithelial cells and rat

lung alveolar macrophages implemented multiple linear regression, linear SVM, radial SVM,

radial basis function neural networks, and general regression neural networks [115]. Radial SVM

separates descriptor data in such a way that in higher dimensions, circular boundaries discretize

particular endpoints. Similarly, radial basis neural networks process descriptors through radial

basis functions and use their summation for prediction. General regression neural networks are a

type of radial basis function that assumes the training sample represents the mean of the

activation functions. This particular study found that the relative performance of each of these

techniques varied depending on the dataset used. For example, linear SVM (R2 = (0.77,0.85), Q2ext = (0.65, 0.96)) and RBFNN (R2 = (0.78,0.88), Q2ext = (0.36,0.91)) performed equivalently

to MLR (R2 = (0.78,0.85), Q2ext = (0.65,0.98)) when using aggregated results from exposure to

TiO2 and ZnO. Optimal models were selected using results from leave-one-out cross validation,

however R2 is reported here for comparability to other studies. When TiO2 was studied

separately, radial SVM presented the highest agreement between predicted and true values

(Radial SVM (R2 = (0.95,0.99), Q2ext = (0.77, 1.00)). When studying ZnO separately, radial

SVM (R2 = (0.94, 1.00), Q2ext = (0.49, 1.00)) and GRNN (R2 = (0.94, 0.98), Q2ext = (0.52, 1.00))

outperformed MLR (R2 = (0.88, 0.97), Q2ext = (0.41, 0.99)). From these results, we identify a

potential need for discrete models for subsets of materials. However, further comparative studies

are needed given that in one particular study, three RBFNN models for three groups of

nanomaterials were found to outperform MLR (MLR: R2 = (0.792,0.857); RBFNN: R2 =

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the sample size and the descriptors selected for the input layer. These two studies differ in both,

with the Papa et. al study featuring a training set of 31 nanoparticles and the Luan et.al study

featuring 15 to 36 nanoparticles per group. Five descriptors were used in the comparative study,

describing concentration and nanoparticle size in varying media. On the other hand, Luan et.al

derived molecular descriptors from software using chemical structure, which could be the reason

for higher accuracy in these RBFNN models.

Besides radial basis functions, neural networks can vary by the model selected for the hidden

layer or by the overall organization of the network. In one study, an asymmetrical sigmoid

function was used in the hidden layer, essentially applying an S-shaped relationship between

inputs and outputs for each node. In this case, chitosan nanoparticles, paired with streptokinase

were applied to Mrc-5 cells and analyzed for cell viability [117]. The input variables, size,

chitosan concentration, solution pH, and stirring time, describe experimental parameters rather

than molecular descriptors. The relatively high agreement for predictions with the training set

(R2 = 0.90) and with the external test set (Q2 = 0.96) indicate the influence of experimental conditions in nanoparticle behavior. Typical neural networks, such as this, benefit from the

implementation of backpropagation, where errors from the output layer are used to update

weights between layers. Another type of neural network that has been applied to nanotoxicology

data is counter-propagation artificial neural networks (CP-ANN), a type of self-organizing map

(SOM). In CP-ANN both unsupervised and supervised methods are used to first discretize

inputs, separating dissimilar training samples, such as in a SOM, and then combining these nodes

into the output layer. In a study using data from Puzyn's study of 17 metal oxide nanoparticles, a

CP-ANN was developed for the cytotoxicity endpoint, as well as a classification endpoint

Figure

Table 1. Summary of Nanomaterial Registry records for biological interactions, adapted from nanomaterialregistry.org
Table 2. Summary of eNanoMapper records for in vitro toxicity data.
Figure 2.1 Conceptual overview of the simulation process and experimental design. Assays were randomly sampled from the original data based on a desired number of assays and assay sources (slices) so that the simulated datasets contained a subset of assays
Figure 2.2 Comparison of Imputation Methods Using ToxPi priority ranks. Mean ToxPi Rank Change between Imputed Simulated Data and Imputed Raw Data
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References

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