Orphan drugs are designed to treat rare diseases, but “rare” is a relative term. Each individual drug approved for an orphan disease could be relevant for as many as 200,000 people. Worldwide, some 300 million people are affected by more than 7,000 orphan diseases.

孤儿药旨在治疗罕见疾病，但“罕见”是一个相对的术语。每一种被批准用于治疗孤儿疾病的药物可能与多达20万人相关。全世界约有3亿人受到7000多种孤儿疾病的影响。

As with all drugs, development costs are high. But, compared to pharmaceuticals for more widespread conditions, the return on investment for orphan drugs is limited because, comparatively, there are fewer potential customers before long. That means it is more important than ever that, when a drug is commercialised, it reaches the right potential customers.

与所有药物一样，开发成本很高。但是，与更广泛使用的药品相比，孤儿药的投资回报率是有限的，因为相比之下，不久以后潜在客户就会减少。这意味着，当一种药物商业化时，它会接触到正确的潜在客户，这一点比以往任何时候都更为重要。

AI-based systems can help companies accomplish this, and save money on highly targeted marketing and sales..

基于人工智能的系统可以帮助公司实现这一目标，并为高度有针对性的营销和销售节省资金。。

Although each drug may target relatively small numbers of patients, orphan drugs are a significant part of the pharmaceutical sector. Nearly half of all drugs now in development target orphan diseases. This is also a market badly in need of new drugs: data shows that one in ten Americans – some 30 million people – are affected by an orphan disease, with no treatment available for over 90% of these illnesses and conditions..

尽管每种药物都可能针对相对较少的患者，但孤儿药是制药行业的重要组成部分。目前正在开发的所有药物中，近一半针对孤儿疾病。这也是一个急需新药的市场：数据显示，十分之一的美国人（约3000万人）受到孤儿疾病的影响，其中90%以上的疾病无法治疗。。

The expense of the orphan drug space

孤儿药空间的费用

Many orphan drugs are developed, at least at the outset, by small pharma companies or start-ups (although bigger firms are beginning to get more involved as well). Whether big or small, though, companies that venture into the orphan drug space are taking on considerable expense. Development costs are similar to those for drugs for more widespread diseases, but, since the potential market is smaller, there is less opportunity to recover costs.

许多孤儿药至少在一开始是由小型制药公司或初创公司开发的（尽管大型公司也开始更多地参与其中）。然而，无论大小，进入孤儿药领域的公司都在承担相当大的费用。开发成本与治疗更广泛疾病的药物相似，但由于潜在市场较小，因此回收成本的机会较少。

That’s one of the reasons the median treatment cost was 17 times higher for orphan than non-orphan drugs. Part of this expense is allayed by the Orphan Drug Act, which in the US provides developers with tax credits of up to 50% of clinical development costs..

这就是为什么孤儿的中位治疗费用比非孤儿药物高17倍的原因之一。《孤儿药法案》减轻了部分费用，该法案在美国为开发商提供了高达临床开发成本50%的税收抵免。。

But, even with those credits, it can cost as much as upwards of $500 million to develop an orphan drug – a significant amount for even a large, established pharma firm, much less a start-up. Add to that commercialisation costs, and the bill goes up to far in excess of a billion dollars. These high stakes make it essential that companies get it right the first time – that development is done in the most successful manner possible, and that the process of commercialising a drug is as efficient as possible..

但是，即使有这些信贷，开发一种孤儿药的成本也可能高达5亿多美元——即使是一家大型的、成熟的制药公司，这也是一笔可观的金额，更不用说一家初创公司了。再加上商业化成本，账单将远远超过10亿美元。。。

The latter is especially challenging; a Deloitte study shows that more than a third of newly- introduced drugs miss their sales expectations in their first year on the market. And some 70% of those drugs continue to miss expectations in subsequent years. The study finds that there are a host of factors that can cause these marketing and sales misses – among them, limited access to markets, a problem endemic to orphan drugs because of the difficulties in zeroing in on a very specific audience of patients and caregivers, along with an inadequate understanding of market needs, and poor product differentiation.

后者尤其具有挑战性；德勤（Deloitte）的一项研究表明，超过三分之一的新推出药物在上市第一年就没有达到预期的销售目标。在随后的几年中，这些药物中约有70%继续没有达到预期。该研究发现，有许多因素可能导致这些营销和销售失误，其中包括市场准入有限，孤儿药特有的问题，因为很难将重点放在非常特定的患者和护理人员群体上，以及对市场需求的理解不足和产品差异化。

Solving these issues is essential for drug companies seeking to introduce new products, especially in the orphan drug space..

。。

The cost-cutting role of AI and machine learning

人工智能和机器学习的成本削减作用

AI can play an important role here. The contributions of AI and machine learning – where algorithms review huge amounts of data seeking patterns that will indicate which molecules and elements are most effective in fighting a disease – are well known. But AI and machine learning also have a crucial role in commercialisation, and can help cut costs and increase chances of success significantly.

AI可以在这里发挥重要作用。人工智能和机器学习的贡献是众所周知的，算法会审查大量的数据寻找模式，这些模式将表明哪些分子和元素在对抗疾病方面最有效。但人工智能和机器学习在商业化中也起着至关重要的作用，可以帮助降低成本，大大增加成功的机会。

This is especially important for companies developing orphan drugs. The Orphan Drug Act’s credits are for discovery and development; companies are largely on their own for commercialisation costs..

这对于开发孤儿药的公司尤其重要。《孤儿药法案》的荣誉是用于发现和开发；公司在很大程度上依靠自己的商业化成本。。

There are numerous ways AI can help with commercialisation. For example, AI systems can help marketing and sales teams discover target populations, including identifying healthcare providers and institutions that specialise in the specific diseases or conditions the drugs seek to treat. Advanced AI can even point drug companies to physicians treating individual patients with certain symptoms and conditions, based on anonymous patient level databases that track patient populations’ medical data (lab results, etc.)..

人工智能可以通过多种方式帮助商业化。例如，人工智能系统可以帮助营销和销售团队发现目标人群，包括确定医疗保健提供者和专门研究药物寻求治疗的特定疾病或病症的机构。先进的人工智能甚至可以根据追踪患者群体医疗数据（实验室结果等）的匿名患者级数据库，将制药公司指向治疗具有某些症状和病症的个体患者的医生。。

Armed with that data, companies can more easily gauge commercial interest, determine sales margins, predict demand, and understand many other factors. Companies can also devise effective marketing campaigns, tailoring messages in a manner appropriate to the groups they are targeting based on this anonymous patient level data, engaging the right healthcare providers at the right time with the right information..

有了这些数据，公司可以更容易地衡量商业利益，确定销售利润率，预测需求，并了解许多其他因素。公司还可以设计有效的营销活动，根据这些匿名患者级别的数据，以适合其目标群体的方式定制信息，在正确的时间让正确的医疗保健提供者获得正确的信息。。

AI systems can also help determine the best channels through which to reach patients and caregivers with marketing and sales messages. Traditionally, companies would have had to do this work manually, taking months – if not longer – to gather and analyse the data and information. With AI and machine learning, they can accomplish these goals in a far shorter period of time – in a matter of hours instead of months..

人工智能系统还可以帮助确定通过营销和销售信息联系患者和护理人员的最佳渠道。传统上，公司必须手动完成这项工作，需要几个月（如果不是更长的话）才能收集和分析数据和信息。有了人工智能和机器学习，他们可以在更短的时间内完成这些目标，只需几个小时而不是几个月。。

AI and accessing the right HCPs, preventing churn

AI和访问正确的HCP，防止流失

AI is also able to analyse more complex data, with many changing and moving parts, which is essential when it comes to understanding the needs of patients dealing with rare diseases and conditions. For example, AI-powered analysis can identify which patients undergoing certain types of treatment are most likely to experience side-effects and require a different course of treatment.

人工智能还能够分析更复杂的数据，其中有许多变化和移动的部分，这对于理解处理罕见疾病和病症的患者的需求至关重要。例如，人工智能支持的分析可以确定哪些接受某些类型治疗的患者最有可能出现副作用并需要不同的治疗过程。

Sales and marketing teams can then target the physicians overseeing these patients, offering the right treatments for the side-effects, and preventing churn. The AI-generated insights from this type of data are especially important when it comes to the ongoing marketing and sales of drugs, beyond their initial launch..

然后，销售和营销团队可以针对监督这些患者的医生，为副作用提供正确的治疗方法，并防止流失。当涉及到药物的持续营销和销售时，人工智能从此类数据中产生的见解尤其重要，而不仅仅是最初的发布。。

The best way for companies – especially smaller ones – to handle this AI analysis is by working with an AI platform designed for pharma commercialisation. Developing an in-house ability to research this data – much less having to find and hire personnel who can manage AI systems – is likely to be prohibitively expensive, raising the already high costs of commercialisation.

对于公司（尤其是较小的公司）来说，处理这种人工智能分析的最佳方法是与专为制药商业化设计的人工智能平台合作。开发内部研究这些数据的能力——更不用说寻找和雇用能够管理人工智能系统的人员了——可能成本过高，从而提高了已经很高的商业化成本。

And, alternatively, the common practice of relying on outside consulting firms to use their AI to undertake such analysis, often leaves drug companies with untimely insights, and too little control over the efforts..

另外，依赖外部咨询公司使用其人工智能进行此类分析的常见做法，往往会让制药公司产生不合时宜的见解，并且对这些努力的控制力太小。。

Unlike with discovery and development costs, which end when the drug is ready for the market, commercialisation costs are ongoing. With advanced, high-quality AI systems, companies developing orphan drugs will be able to focus their efforts and cut these ongoing commercialisation costs significantly.

与发现和开发成本不同，发现和开发成本在药物上市时结束，商业化成本正在持续。凭借先进、高质量的人工智能系统，开发孤儿药的公司将能够集中精力，大幅削减这些正在进行的商业化成本。

Companies will also be able to dynamically and continuously adjust their sales and marketing strategy and tactics, as well as their messaging and targets, based on changing market needs. Hopefully those lower costs will leave more money to keep developing new innovative drugs, as well as encourage more companies to develop treatments for the thousands of rare diseases that currently have no treatment at all..

公司还将能够根据不断变化的市场需求，动态不断地调整其销售和营销战略和策略，以及信息和目标。希望这些较低的成本将留下更多的资金来继续开发新的创新药物，并鼓励更多的公司开发针对目前根本没有治疗方法的数千种罕见疾病的治疗方法。。