The growing world population and its impact on the environment creates a need for advanced agriculture technologies and products, for example, highly target-specific animal feed additives to improve the condition and growth of livestock1,2,3. Mycotoxin detoxifiers (MDTs) are an example of such additives4,5. Mycotoxins are toxic secondary metabolites typically produced by fungi growing in food and animal feed commodities6,7, and exposure to these contaminants may result in death or disease8,9. The maximum levels of some of the most highly prevalent mycotoxins in animal feed are already controlled guidance values or recommendations, rendering the need for mitigation strategies, one of which is mycotoxin capture by detoxifier feed additives10. The global market of the latter is projected to grow at the rate of 3.1% from 2020, and reaching a value of USD 3.1 billion by 202711,12,13,14.
A typical mode of action of MDTs involves either forming bulky non-absorbable complexes with mycotoxins in the gastrointestinal tract, hence reducing their oral bioavailability, or promoting their degradation into non-toxic metabolites by bio-transforming agents, such as bacteria or enzymes1,15,16. Adsorbents capture mycotoxins which are delivered through the gastrointestinal tract of the animals with their feed, and the mycotoxin-MDT complex is eliminated with feces thus minimizing the absorption in the blood stream17. Among the mycotoxin-binding agents (binders), inorganic porous materials such as clays minerals are recognized effective especially for sequestering of aflatoxin B1 (AFB1)18,19. However, the specific mode of action strongly depends on the affinity between clay and mycotoxins, which is a function of multiple variables representing the structure and properties of the mycotoxins, the adsorbing materials as well as the process, e.g. inclusion rate of MDTs18. For example, montmorillonites are commonly used for aflatoxins binding by creating complexes with the exchangeable cations, while stevensites exploit their larger surface area for efficient entrapping of ochratoxin A (OTA) and zearalenone (ZEN)20,21. Material processing strategies such as the preparation of organo-aluminosilicates have made improvement in the uptake capability of ZEN, OTA and T-2 toxin (T2)22. Similarly, other porous materials such as activated charcoal (AC) have been recognized as strong adsorbents for several mycotoxins, including deoxynivalenol (DON)23. However, the required AC doses necessary for a significant detoxification leads to sequestrating of essential micronutrients such as vitamins or minerals24. Composite materials based on a mixture of the above may offer to capture and remove multiple large number of mycotoxins while minimizing the interference effect. Besides the regulated mycotoxins, yet-unregulated and emerging mycotoxins occur frequently in agricultural products25, thus it is desired that the detoxifier design should address a wide variety of toxic fungal metabolites without compromising the animal health26.
In our approach, the information on MDT performance is a function of descriptor space which encodes clay materials with experimentally-determined physico-chemical features, mycotoxins with molecular descriptors, and the modeled in vitro experiments with five setting parameters (Fig. 1a). Then, we develop random forest regressor models (RF) predicting both Ads(%) (denoted RFads) and Eff(%) (denoted RFeff) using 84 experimental data points (Fig. 1b). The data distribution shown in Supplementary Fig. S1, demonstrates the low correlation between in vitro adsorption, desorption and efficiency outcomes contained in the dataset. The dataset comprises 15 diverse toxin-detoxifier materials (MDTs), i.e., 10 natural clays and 5 clay-based composites in which formulation the organic component is activated charcoal (AC) (see method section for details). The two independent machine learning models exhibit high predictive performances with R2 score of 0.92 and 0.96 for RFads and RFeff, respectively (details in method section). Upon the positive assessment of the models, three main contributions were investigated, i.e., screening of optimal solutions, wide in vitro detoxification assessment, and mode of action capturing (Fig. 1c). Finally, biomarker detection-based in vivo trial in broiler chickens was performed as proof-of-concept to validate our approach, hence, the sequestration power of the top powerful MDT towards the sequestration of a mycotoxin (DON) selected among the most challenging to be mitigated (Fig. 1d).
Response of predicted in vitro efficiency of SEP/MONT/AC towards the removal of the explored mycotoxin groups (a). The predictions were obtained by RF fixing the experimental setting to 2 kg/t of inclusion rate of SEP/MONT/AC, 2 µg/ml of toxin concentration, adsorption and desorption pH to 3 and 6.5, respectively. Graphical representation of in vivo design of experiment validating the detoxification of DON by SEP/MONT/AC detoxifier (b). Mean plasma concentration and standard deviation of DON after single oral bolus administration of DON alone (0.5 mg/kg BW) and DON in combination with SEP/MONT/AC (0.4 g/kg BW) to 8 broiler chickens (c). Mean response and standard deviation of deoxynivalenol-sulphate (DON-S) in plasma after single oral bolus administration of DON alone (0.5 mg/kg BW) and DON in combination with SEP/MONT/AC (0.4 g/kg BW) to 8 broiler chickens (d).
We have demonstrated a machine learning-aided approach to the design of mycotoxin detoxifiers (MDTs). We used a dataset of experimental in vitro assessment of the adsorption and efficiency of various MDTs against regulated mycotoxins (DON, T2, ZEN, OTA, FB1, AFB1) to build two random forest (RF) models which predict the adsorption (RFads) and efficiency (RFeff). The model feature space was defined by descriptors representing MDT and mycotoxin physico-chemical properties and in vitro experimental setting which modelled the animal gastrointestinal tract. The model achieved satisfactory performance with R2 of 0.92 and 0.96 and MAE of 5.5% and 4.8% for RFads and RFeff, respectively. By means of feature importance analysis of the models, we gain insights into the MDT mode of action i.e., the unequivocal heavy contribution of Mg2+ suggests that the main mechanism processes through substitutions of the exchangeable cations and chelate formations. Our RFeff being skilled to capture the synergy between components MDT formulations was employed in the recognizing of specific material preparation which avoids interferences originated by micronutrient adsorption. The top promising formulation incorporates sepiolite, montomorillonite and activated carbon in ratio 1:4:0.1 (SEP/MONT/AC). Our RFeff targets DON as the most challenging to-be-removed molecule which was trained for the in vivo validation of the real performance of SEP/MONT/AC on the oral bioavailability of DON in broiler chickens. The specifically designed in vivo trial avoids the repetition of pointless use of animals following the 3Rs principles. Both DON and the corresponding phase II metabolite (DON-S) detection demonstrated the relevant reduction in in vivo systemic exposure to DON in broiler chickens, confirming the findings of our approach, i.e., the capability of the identified material to mitigate the presence of the challenging to be removed DON. Our computer-aided approach enables versatile applications, e.g. assisting the recommendation of the precise administration dosage of MDTs for specific contamination levels, and providing the estimation of adsorption efficiency of MDTs against yet-to-be-regulated mycotoxins projecting future assessment of emerging contaminants.
A number of hybrid formulations were screened for searching of optimal detoxifier material. RF predicted the efficiency allowing to identify the top promising formulation in composite materials composed by SEP, MONT and AC organic compound. A set of diverse formulations was proposed fixing the inorganic portion to SEP/MONT 1:4 and varying the AC from 0 to 5% with respect to the composite weight. Each material descriptor value of the proposed formulations was linearly calculated as \(\sum\nolimits_\texti \texta_\texti \textx_\texti \), where ai is the amount (%) of the i-component in the hybrid formulations and xi is the descriptor value corresponding to the pure i-component.
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A detoxifier kidney replaces the user's organic kidney. The detoxifier kidney has a part efficiency of 110%. This not only fully replaces the functionality of a normal kidney - when coupled with the kidney body part having a Blood filtration importance of 50%, it results in an increase in blood filtration of +5% over natural per kidney replaced.[Verify] Blood filtration, in turn, improves Immunity Gain Speed by +2.5%.[Verify]
By installing a detoxifier kidney, the pawn's natural kidney, if perfectly healthy, is harvested without a penalty. This organic kidney can then be sold or stored for later use. A detoxifier kidney prevents installation of implants into the kidney, namely the immunoenhancer.
A detoxifier kidney increases Toxic Resistance by +50%. This reduces the amount of toxic buildup from all sources by 50%. Replacing both kidneys stacks to the maximum of +100% resistance. Detoxifier kidneys are also immune to chemical damage from sources such as yayo, and will cure it the damaged kidney is replaced.
A pair of detoxifier lungs and a pair of detoxifier kidneys both provide 100% resistance against toxic buildup. The lung instead provides Toxic Environment Resistance, so it protects against rot stink. The lung will stack additively with face masks, for 100% protection with 1 lung and face mask. However, kidneys stack multiplicatively, resulting in 75% protection with 1 kidney and face mask. Unlike the kidney, the lung's Environmental Resistance does not protect against direct toxic attacks, like a cobra's bite, but those are rare. Lungs require 15 Plasteel, 4 Advanced components per lung, 2 more advanced components than a kidney.