When pharma leaders gather to decide what molecules they want to prioritize, it can feel a little like being stuck in a traffic jam. Everyone has their own destination in mind, and they are all in a hurry to get there, but somehow there is always gridlock.
If you’ve ever been a part of the clinical development decision-making process, you know that feeling.
Without data to identify the right development strategy, based on the company’s timeline, risk-appetite, market demands and competitive climate, decision-making involves a lot of conversations and arguments between stakeholders who often have opposing points of view.
That’s not surprising, because each stakeholder brings a different perspective to the table. Executives want their products to deliver the biggest returns. Portfolio managers have limited resources to spread across many projects. Seasoned internal clinical teams base their judgement on what worked in previous trials. And key opinion leaders (KOLs) tend to bring their own focused specialties.
All of these stakeholders bring valid opinions to the discussion, but they also all have biases based on their preferences and personal experience. And because it can take a decade or more to bring even a single drug to market, even knowledgeable experts may only be drawing on a handful of trials to base their decisions.
这种“体验偏见”可以减慢决策过程，更糟糕的是，导致最大或最有影响力的利益相关者赢得了这一论点。这可能会导致做出错误的决定，并对公司的发展成本，时间表和未来成功产生巨大影响。
Bring data to the discussion
The best way to beat experience bias is by basing decisions on hard data. The wealth of global clinical information currently available around costs, timing and risks provides a powerful method to help decision makers temper their own experience with facts.
When pharma companies use data and analytic technology, they can assess each molecule based on its anticipated development costs, potential risk, projected time to deliver, and commercial value. Then they can make trade-offs among those inputs based on latest market trends, unmet medical needs, current trial results, and the commercial value of competing drugs.
这使他们能够对每种开发路径的结果进行建模，但仍将所有利益相关者的意见纳入其中，以便他们可以看到每个首选计划的可能结果。团队可以承认自己的不同背景，同时仍基于最能支持公司目标的模型选择正确的课程。
When pharma brings data to this process, it shortens the time to a good decision, and removes the bias and emotion, leaving only facts to guide the way. It also gives decision makers greater confidence that the molecule they chose will deliver the desired business results.
成本，风险或收入：您会选择哪个？
We’ve helped many clients eliminate uncertainty and bias through this data-driven approach, and it is transforming the way they develop new drugs.
例如，一家具有有前途的免疫蛋白酶体的新兴生物技术公司提出了有关相关竞争格局和试验基准的问题。客户希望评估两个治疗领域的五个孤儿指示中的选择。
我们的团队使用了管道架构师为了分析所需指示的竞争格局和试验基准，为每种情况的最佳和最坏情况进行建模。我们提供的解决方案突出了开发时间，成本，风险和商业预测之间的权衡，使客户可以选择与其战略目标保持一致的指示。这有助于他们决定放弃三个迹象，并优先考虑这两个最有希望的资产。
In another case, we worked with a pharma company that had obtained a risk-adjusted Net Present Value (eNPV) for a molecule but wanted an independent review of the plan.
在审查了核心数据集后，我们确定ENPV评估依赖于尚未考虑临床开发计划的关键细节的内部假设。结果，他们的技术成功率和价值估计的可能性比预计的数据高约20％ - 向ENPV贡献了8600万美元。然后，我们的团队通过提出的设计更改领导他们，使客观确定的技术成功概率（PTS）从15％增加到31％，而ENPV则增加了9100万美元。
大型医疗保健数据和分析技术的进步lol买外围用什么软件意味着公司不必仅依靠直觉和过去的经验来指导它们 - 也不应该必威官方在线。在临床开发中使用资产评估模型为决策带来了精确的精确度，使制药公司能够打破与以意见为中心的会议相关的僵局，并减少经验偏见的影响。这种方法节省了时间，降低了风险，并使决策者相信他们正在为公司，患者和品牌做出最佳选择。
要了解有关IQVIA的管道架构师平台的更多信息here或与我们联系pipelinearchitect@iqvia.com