With the irreplaceable role which pharmaceuticals have in medicine it is important to keep in mind the frequently negative effects of their production and release can have on the environment. Unknown compounds which represent potentially new pharmaceutical products are therefore important to study given the lack of experimental data that exists for their potential harmfulness to the environment and the health of humans. In order to maximally protect the environment and human health in the production, usage and disposal of pharmaceuticals it is important to get the best possible results about their harmfulness. The problem lies in the fact that the number of new organic compounds synthesized in the industry is enormous and classical testing is expensive, time consuming and potentially ethically problematic in regards to the usage of test animals. Hence there is a striving to use computational estimates of potential harmfulness of new compounds. For the estimation of the possible harmfulness of the new synthetized compounds LD-1 and LD-2, amidino substituted benzothiazoles, they were exposed to oxidative, acidic and basic conditions in addition to being exposed to UV/VIS radiation and high temperatures in order to bring about their forced degradations. Samples treated in such a way were analyzed with the help of a 2D-UHPLC-QTOF system and from the resulting chromatograms and mass spectra their degradation products are assumed and degradation pathways were suggested. The starting compounds LD-1 and LD-2 as well as their potential degradation products were analyzed using T.E.S.T. software package in order to estimate the bioaccumulation factor, developmental toxicity and mutagenicity. T.E.S.T. is a program package which the American environmental protection agency has released for free as a piece of software for the estimation of the danger organic chemicals pose to human health and the environment. The mathematical models which the estimation methods incorporated into T.E.S.T. compare the analyzed molecules with known molecules in the database based on a large number of physical descriptors. This approach allows for quick, cheap and simple risk estimates of a molecule for human health or the environment.