Looking for the LOAEL or NOAEL Concentration of Nickel-Oxide Nanoparticles in a Long-Term Inhalation Exposure of Rats

The pulmonary effects of nickel-containing nanoparticles: Cytotoxicity, genotoxicity, carcinogenicity, and their underlying mechanisms

With the exponential growth of the nanotechnology field, the global nanotechnology market is on an upward track with fast-growing jobs. Nickel (Ni)-containing nanoparticles (NPs), an important class of transition metal nanoparticles, have been extensively used in industrial and biomedical fields due to their unique nanostructural, physical, and chemical properties. Millions of people have been/are going to be exposed to Ni-containing NPs in occupational and non-occupational settings. Therefore, there are increasing concerns over the hazardous effects of Ni-containing NPs on health and the environment. The respiratory tract is a major portal of entry for Ni-containing NPs; thus, the adverse effects of Ni-containing NPs on the respiratory system, especially the lungs, have been a focus of scientific study. This review summarized previous studies, published before December 1, 2023, on cytotoxic, genotoxic, and carcinogenic effects of Ni-containing NPs on humans, lung cells in vitro, and rodent lungs in vivo, and the potential underlying mechanisms were also included. In addition, whether these adverse effects were induced by NPs themselves or Ni ions released from the NPs was also discussed. The extra-pulmonary effects of Ni-containing NPs were briefly mentioned. This review will provide us with a comprehensive view of the pulmonary effects of Ni-containing NPs and their underlying mechanisms, which will shed light on our future studies, including the urgency and necessity to produce engineering Ni-containing NPs with controlled and reduced toxicity, and also provide the scientific basis for developing nanoparticle exposure limits and policies.

Toxicity dose descriptors from animal inhalation studies of 13 nanomaterials and their bulk and ionic counterparts and variation with primary particle characteristics

This study collects toxicity data from animal inhalation studies of some nanomaterials and their bulk and ionic counterparts. To allow potential grouping and interpretations, we retrieved the primary physicochemical and exposure data to the extent possible for each of the materials. Reviewed materials are compounds (mainly elements, oxides and salts) of carbon (carbon black, carbon nanotubes, and graphene), silver, cerium, cobalt, copper, iron, nickel, silicium (amorphous silica and quartz), titanium (titanium dioxide), and zinc (chemical symbols: Ag, C, Ce, Co, Cu, Fe, Ni, Si, Ti, TiO₂, and Zn). Collected endpoints are: a) pulmonary inflammation, measured as neutrophils in bronchoalveolar lavage (BAL) fluid at 0-24 hours after last exposure; and b) genotoxicity/carcinogenicity. We present the dose descriptors no-observed-adverse-effect concentrations (NOAECs) and lowest-observed-adverse-effect concentrations (LOAECs) for 88 nanomaterial investigations in data-library and graph formats. We also calculate 'the value where 25% of exposed animals develop tumors' (T25) for carcinogenicity studies. We describe how the data may be used for hazard assessment of the materials using carbon black as an example. The collected data also enable hazard comparison between different materials. An important observation for poorly soluble particles is that the NOAEC for neutrophil numbers in general lies around 1 to 2 mg/m³. We further discuss why some materials' dose descriptors deviate from this level, likely reflecting the effects of the ionic form and effects of the fiber-shape. Finally, we discuss that long-term studies, in general, provide the lowest dose descriptors, and dose descriptors are positively correlated with particle size for near-spherical materials.

The Application of Reference Dose Prediction Model to Human Health Water Quality Criteria and Risk Assessment

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model.

Reproductive toxicity of maternal exposure to di(2-ethylhexyl)phthalate and butyl paraben (alone or in association) on both male and female Wistar offspring

Parabens and phthalates are commonly found as contaminants in human fluids and are able to provoke reproductive toxicity, being considered endocrine disruptors. To evaluate the effects of phthalate and paraben, alone or in combination, on reproductive development of the offspring, female pregnant Wistar rats were allocated in six experimental groups: Three control groups (gavage [CG], subcutaneous [CS], and gavage + subcutaneous) received corn oil as vehicle, and the remaining groups were exposed to di(2-ethylhexyl)phthalate (DEHP) (500 mg/kg, gavage), butyl paraben (BP) (100 mg/kg, subcutaneously), or MIX (DEHP + BP), from Gestational Day 12 until Postnatal Day (PND) 21. The following parameters were assessed on the offspring: anogenital distance and weight at PND 1, nipple counting at PND 13, puberty onset, estrous cycle, weights of reproductive and detoxifying organs, histological evaluation of reproductive organs, and sperm evaluations (counts, morphology, and motility). Female pups from MIX group presented reduced body weight at PND 1, lower AGD, and decreased endometrium thickness. Male animals showed decreased body weight at PND 1 and lower number of Sertoli cells on DEHP and MIX groups, MIX group revealed increase of abnormal seminiferous tubules, DEHP animals presented delayed preputial separation and higher percentage of immotile sperms, and BP males presented diminished number of Leydig cells. In conclusion, the male offspring was more susceptible to DEHP toxicity; even when mixed to paraben, the main negative effects observed seem to be due to antiandrogenic phthalate action. On the other hand, DEHP seems to be necessary to improve the effects of BP on reducing estrogen-dependent and increasing androgen-dependent events.

Reference dose prediction by using CDK molecular descriptors: A non-experimental method

Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.