It depends on which part of their approch you focus on. I'd expect their machine translation model to be sufficiently general to support basically any language given enough training data. Yes, the languages you listed have some edge cases, but so do languages they already support fine. For example, the lack of spaces separating words in Chinese and Japanese can be handled by the same word-piece segmentation they need for German compound words.

The bigger problem is likely to be lack of training data. Unless they have a pile of cash to pay professional translators to produce a parallel corpus, the alternative is to scrape translations from the internet. Basically, crawl the same site multiple times with different Accept-Language headers and try to align the results. Crucially, this depends on an existing ecosystem of bilingual websites with high-quality human translations.

According to DeepL's website they're a spin-off of Linguee, who provide a search service for exactly that kind of parallel data. So before DeepL starts supporting any given language pair, you should expect it to appear in Linguee first. https://en.wikipedia.org/wiki/Linguee

Edit: It took me a while to figure out how to select a different language on https://linguee.com (Their UI seems broken using mobile Firefox Preview.) Appending /english-chinese and /english-japanese to the URL shows that they already support those two, and the alignment of translations appears reasonable to me. No /english-arabic, though.